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shao

This commit is contained in:
shao1chuan 2024-02-27 11:40:20 +08:00
parent 66e54a00aa
commit 750686d84d
34 changed files with 27798 additions and 11 deletions
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2
.idea/misc.xml
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@ -3,5 +3,5 @@
<component name="JavaScriptSettings">
<option name="languageLevel" value="ES6" />
</component>
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (regression)" project-jdk-type="Python SDK" />
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (myvenv)" project-jdk-type="Python SDK" />
</project>

2
.idea/pythonbook.iml
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@ -4,7 +4,7 @@
<content url="file://$MODULE_DIR$">
<excludeFolder url="file://$MODULE_DIR$/venv" />
</content>
<orderEntry type="jdk" jdkName="Python 3.11 (regression)" jdkType="Python SDK" />
<orderEntry type="jdk" jdkName="Python 3.11 (myvenv)" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
</module>

6
人与鳄鱼/人与鳄鱼问题.py
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@ -24,7 +24,7 @@ for x in range(100):
turtle.color("black")
turtle.penup()
turtle.goto(positionHuman)
turtle.dot(2, "yellow")
turtle.dot(2, "brown")
turtle.pendown()
turtle.setheading(escapeDregree)
@ -65,10 +65,10 @@ for x in range(100):
turtle.fd(20)
positionLion2 = turtle.position()
turtle.color("yellow")
turtle.color("brown")
turtle.penup()
turtle.goto(positionLion3)
turtle.dot(2, "yellow")
turtle.dot(2, "brown")
turtle.pendown()
positionLion3ToHuman = turtle.towards(positionHuman)
turtle.setheading(positionLion3ToHuman)

3
人与鳄鱼/人与鳄鱼，步骤1 画简单图.py
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@ -17,9 +17,10 @@ turtle.goto(human)
turtle.dot(2, "red")
turtle.pendown()
turtle.setheading(angle)
turtle.fd(10)
turtle.fd(100)
human = turtle.position()
print(human)
turtle.color("green")
turtle.penup()

6
优化算法/梯度下降.py
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@ -9,14 +9,14 @@ x2_history = []
fName = 'himmelblau'
dfName = 'df_'+fName
def objective(trial):
x1 = 0.1
x2 = 0.1
x1 = -0.3
x2 = -3
lr = 0.002
# x1 = trial.suggest_float("x1", 0, 3)
# x2 = trial.suggest_float("x2", 0, 3)
beta = 0.5
# lr = trial.suggest_float("lr", 0, 0.1)
epochs = 15
epochs = 100
z = eval(fName)(x1, x2)
z_history.append(z)

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实例学习Pynimate/file.gif Normal file
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2
实例学习Pynimate/readme Normal file
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@ -0,0 +1,2 @@
https://julkaar9.github.io/pynimate/#how-to-use
https://github.com/julkaar9/pynimate

28
实例学习Pynimate/test.py Normal file
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@ -0,0 +1,28 @@
# import matplotlib if you wish to see the animation in gui
import pandas as pd
from matplotlib import pyplot as plt
import pynimate as nim
df = pd.DataFrame(
{
"time": ["1960-01-01", "1961-01-01", "1962-01-01"],
"Afghanistan": [1, 2, 3],
"Angola": [2, 3, 4],
"Albania": [1, 2, 5],
"USA": [5, 3, 4],
"Argentina": [1, 4, 5],
}
).set_index("time")
cnv = nim.Canvas()
# Interpolation frequency is 2 days
bar = nim.Barhplot.from_df(df, "%Y-%m-%d", "2d")
# use set_time to draw the datetime in the canvas
# here we are using a callback that returns datetime formatted in month, year
bar.set_time(callback=lambda i, datafier: datafier.data.index[i].strftime("%b, %Y"))
# add the bar plot to the canvas
cnv.add_plot(bar)
cnv.animate()
cnv.save("file", 24, "gif")
# plt.show()

3
实例学习d3blocks/readme Normal file
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@ -0,0 +1,3 @@
https://mp.weixin.qq.com/s/dLGK6DOj-sFfQSieXs5G5w
官网
https://d3blocks.github.io/d3blocks/pages/html/Timeseries.html#chart

30
实例学习d3blocks/test.py Normal file
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# 导入d3blocks
from d3blocks import D3Blocks
import numpy as np
# 初始化
d3 = D3Blocks()
# 数据准备
df = d3.import_example('mnist')
size = np.random.randint(0, 8, df.shape[0])
opacity = np.random.randint(0, 8, df.shape[0]) / 10
tooltip = df['y'].values.astype(str)
# 绘图
d3.scatter(
df['PC1'].values, # PCA图主成分PC1 x轴数据
df['PC2'].values, # PCA图主成分PC2 y轴数据
x1=df['tsne_1'].values, # tSNE图x轴数据
y1=df['tsne_2'].values, # tSNE图y轴数据
color=df['y'].values.astype(str),
tooltip=tooltip, # 设置点的值
size=size,
opacity=opacity, # 设置点透明度
stroke='#000000',
cmap='tab20', # 设置Colormap
scale=True,
label_radio=['PCA', 'tSNE'],
figsize=[1024, 768],
filepath='scatter_demo.html', #设置结果名称，结果为网页html格式
)

12
实例学习d3blocks/test2.py Normal file
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from d3blocks import D3Blocks
d3 = D3Blocks()
df = d3.import_example('energy')
d3.sankey(df,
title='Sankey - d3blocks',
filepath='sankey.html',
figsize=(800, 600),
node={"align": "justify", "width": 15, "padding": 15, "color": "currentColor"},
link={"color": "source-target", "stroke_opacity": 0.5},
margin={"top": 5, "right": 1, "bottom": 5, "left": 1},
showfig=True,
overwrite=True)

388
实例学习d3blocks/test3.py Normal file
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@ -0,0 +1,388 @@
"""Scatter block.
Library : d3blocks
Author : E.Taskesen
Mail : erdogant@gmail.com
Github : https://github.com/d3blocks/d3blocks
License : GPL3
"""
# import colourmap
import numpy as np
from jinja2 import Environment, PackageLoader
from pathlib import Path
import os
import time
try:
from ..utils import set_colors, convert_dataframe_dict, set_path, update_config, write_html_file
except:
from utils import set_colors, convert_dataframe_dict, set_path, update_config, write_html_file
# %% Set configuration properties
def set_config(config={}, **kwargs):
"""Set the default configuration setting."""
logger = kwargs.get('logger', None)
config['chart'] = 'Scatter'
config['title'] = kwargs.get('title', 'scatter - D3blocks')
config['filepath'] = set_path(kwargs.get('filepath', 'scatter.html'), logger)
config['showfig'] = kwargs.get('showfig', True)
config['overwrite'] = kwargs.get('overwrite', True)
config['figsize'] = kwargs.get('figsize', [900, 600])
config['cmap'] = kwargs.get('cmap', 'tab20')
config['scale'] = kwargs.get('scale', False)
config['ylim'] = kwargs.get('ylim', [None, None])
config['xlim'] = kwargs.get('xlim', [None, None])
config['label_radio'] = kwargs.get('label_radio', ['(x, y)', '(x1, y1)', '(x2, y2)'])
config['color_background'] = kwargs.get('color_background', '#ffffff')
config['reset_properties'] = kwargs.get('reset_properties', True)
config['notebook'] = kwargs.get('notebook', False)
config['jitter'] = kwargs.get('jitter', None)
# Return
return config
# %% Preprocessing
def check_exceptions(x, y, x1, y1, x2, y2, size, color, tooltip, logger):
"""Check Exceptions."""
# if len(config['label_radio'])!=sum(list(map(lambda x: x=='', config['radio_button_visible']))): raise Exception(logger.error('input parameter [label_radio] must contain the correct number of labels depending on the (x,y), (x1,y1), (x2,y2) coordinates.'))
if len(x) != len(y): raise Exception(logger.error('input parameter [x] and [y] should be of size of (x, y).'))
if size is None: raise Exception(logger.error('input parameter [size] should have value >0.'))
if color is None: raise Exception(
logger.error('input parameter [color] should be of a list of string with hex color, such as "#000000".'))
if isinstance(size, (list, np.ndarray)) and (len(size) != len(x)): raise Exception(
logger.error('input parameter [s] should be of same size of (x, y).'))
if (tooltip is not None) and len(tooltip) != len(x): raise Exception(
logger.error('input parameter [tooltip] should be of size (x, y) and not None.'))
if (x1 is not None) or (y1 is not None):
if len(x1) != len(y1): raise Exception(logger.error('input parameter [x1] should be of size of (x1, y1).'))
if len(x) != len(x1): raise Exception(logger.error('input parameter (x1, y1) should be of size of (x, y).'))
if (x2 is not None) or (y2 is not None):
if len(x2) != len(y2): raise Exception(logger.error('input parameter [x2] should be of size of (x2, y2).'))
if len(x) != len(x2): raise Exception(logger.error('input parameter (x2, y2) should be of size of (x, y).'))
# %% Set the Node properties
def set_node_properties(*args, **kwargs):
"""Set the node properties."""
return None
# %% Set the edge properties
def set_edge_properties(*args, **kwargs):
"""Set the edge properties for the scatterplot block.
Parameters
----------
x : numpy array
1d coordinates x-axis.
y : numpy array
1d coordinates y-axis.
x1 : numpy array
Second set of 1d coordinates x-axis.
y1 : numpy array
Second set of 1d coordinates y-axis.
x2 : numpy array
Third set of 1d coordinates x-axis.
y2 : numpy array
Third set of 1d coordinates y-axis.
size: list/array of with same size as (x,y). Can be of type str or int.
Size of the samples.
color: list/array of hex colors with same size as (x,y)
'#ffffff' : All dots are get the same hex color.
None: The same color as for c is applied.
['#000000', '#ffffff',...]: list/array of hex colors with same size as (x,y)
stroke: list/array of hex colors with same size as (x,y)
Edgecolor of dotsize in hex colors.
'#000000' : All dots are get the same hex color.
['#000000', '#ffffff',...]: list/array of hex colors with same size as (x,y)
c_gradient : String, (default: None)
Make a lineair gradient based on the density for the particular class label.
'#ffffff'
tooltip: list of labels with same size as (x,y)
labels of the samples.
opacity: float or list/array [0-1]
Opacity of the dot. Shoud be same size as (x,y)
cmap : String, (default: 'inferno')
All colors can be reversed with '_r', e.g. 'binary' to 'binary_r'
'Set1','Set2','rainbow','bwr','binary','seismic','Blues','Reds','Pastel1','Paired','twilight','hsv'
scale: Bool, optional
Scale datapoints. The default is False.
Returns
-------
d3.edge_properties: DataFrame of dictionary
Contains properties of the unique input edges/links.
"""
# Collect arguments
if len(args) == 2:
x, y = args
else:
x = kwargs.get('x', None)
y = kwargs.get('y', None)
# Collect key-word arguments
x1 = kwargs.get('x1', None)
y1 = kwargs.get('y1', None)
x2 = kwargs.get('x2', None)
y2 = kwargs.get('y2', None)
jitter = kwargs.get('jitter', None)
size = kwargs.get('size', 5)
color = kwargs.get('color', '#69b3a2')
stroke = kwargs.get('stroke', '#000000')
c_gradient = kwargs.get('c_gradient', None)
tooltip = kwargs.get('tooltip', None)
opacity = kwargs.get('opacity', 0.8)
cmap = kwargs.get('cmap', 'tab20')
scale = kwargs.get('scale', False)
logger = kwargs.get('logger', None)
if isinstance(size, list): size = np.array(size)
# if (x1 is None): x1 = x
# if (y1 is None): y1 = y
# if (x2 is None): x2 = x
# if (y2 is None): y2 = y
if (x1 is None): x1 = np.zeros_like(x) * np.nan
if (y1 is None): y1 = np.zeros_like(x) * np.nan
if (x2 is None): x2 = np.zeros_like(x) * np.nan
if (y2 is None): y2 = np.zeros_like(x) * np.nan
# Add jitter
if jitter is not None:
x = x + np.random.normal(0, jitter, size=len(x))
if y is not None: y = y + np.random.normal(0, jitter, size=len(y))
if x1 is not None: x1 = x1 + np.random.normal(0, jitter, size=len(x1))
if x2 is not None: x2 = x2 + np.random.normal(0, jitter, size=len(x2))
if y1 is not None: y1 = y1 + np.random.normal(0, jitter, size=len(y1))
if y2 is not None: y2 = y2 + np.random.normal(0, jitter, size=len(y2))
# Combine into array
X = np.c_[x, y]
# Combine second coordinates into array
X1 = np.c_[x1, y1]
X2 = np.c_[x2, y2]
# Scale data
if scale:
if logger is not None: logger.info('Scaling xy-coordinates.')
X = _scale_xy(X)
X1 = _scale_xy(X1)
X2 = _scale_xy(X2)
# In case only one (s)ize is defined. Set all points to this size.
if isinstance(size, (int, float)): size = np.repeat(size, X.shape[0])
if np.any(size < 0):
if logger is not None: logger.info('[%.0d] sizes are <0 and set to 0.' % (np.sum(size < 0)))
size[size < 0] = 0
# In case None tooltip is defined. Set all points to this tooltip.
if tooltip is None: tooltip = np.repeat('', X.shape[0])
# In case only one opacity is defined. Set all points to this size.
if isinstance(opacity, (int, float)): opacity = np.repeat(opacity, X.shape[0])
# colors
color, labels = set_colors(X, color, cmap, c_gradient=c_gradient)
# In case stroke is None: use same colors as for c.
if stroke is None:
stroke = color
elif isinstance(stroke, str):
# In case only one stroke is defined. Set all points to this size.
stroke = np.repeat(stroke, X.shape[0])
# Make dict with properties
dict_properties = {}
for i in range(0, X.shape[0]):
dict_properties[i] = {'label': labels[i], 'x': X[i][0], 'y': X[i][1], 'x1': X1[i][0], 'y1': X1[i][1],
'x2': X2[i][0], 'y2': X2[i][1], 'color': color[i], 'size': size[i], 'stroke': stroke[i],
'opacity': opacity[i], 'tooltip': tooltip[i]}
# Create the plot
# df = pd.DataFrame(dict_properties).T
# return
return dict_properties
# %% Scale data
def _scale_xy(X):
"""Scale xy coordinates."""
x_min, x_max = np.min(X, 0), np.max(X, 0)
return (X - x_min) / (x_max - x_min)
# %% Show
def show(df, **kwargs):
"""Build and show the graph.
Parameters
----------
df : pd.DataFrame()
Input data.
label_radio: List ['(x, y)', '(x1, y1)', '(x2, y2)']
The labels used for the radiobuttons.
set_xlim : tuple, (default: [None, None])
Width of the x-axis: The default is extracted from the data with 10% spacing.
set_ylim : tuple, (default: [None, None])
Height of the y-axis: The default is extracted from the data with 10% spacing.
title : String, (default: None)
Title of the figure.
'Scatterplot'
filepath : String, (Default: user temp directory)
File path to save the output.
'c://temp//Scatter_demo.html'
figsize : tuple, (default: [None, None])
Size of the figure in the browser, [width, height].
[900, 600]
showfig : bool, (default: True)
True: Open browser-window.
False: Do not open browser-window.
overwrite : bool, (default: True)
True: Overwrite the html in the destination directory.
False: Do not overwrite destination file but show warning instead.
reset_properties : bool, (default: True)
True: Reset the node_properties at each run.
False: Use the d3.node_properties()
config : dict
Dictionary containing configuration keys.
logger : Object, (default: None)
Show messages on screen.
Returns
-------
config : dict
Dictionary containing updated configuration keys.
"""
df = df.copy()
logger = kwargs.get('logger', None)
config = update_config(kwargs, logger)
config = config.copy()
# Convert dict/frame.
df = convert_dataframe_dict(df, frame=True)
# Set the radio button and visibility of the labels
config['radio_button_visible'] = [("display:none;" if (np.all(list(map(np.isnan, df['x1'])))) else ""),
("display:none;" if (np.all(list(map(np.isnan, df['x1'])))) else ""),
("display:none;" if (np.all(list(map(np.isnan, df['x2'])))) else "")]
if ("display:none" in config['radio_button_visible'][0]): config['label_radio'][0] = ""
if ("display:none" in config['radio_button_visible'][1]): config['label_radio'][1] = ""
if len(config['label_radio']) == 3 and ("display:none" in config['radio_button_visible'][2]):
config['label_radio'][2] = ""
elif len(config['label_radio']) == 2:
config['label_radio'].append("")
# Compute xlim and ylim for the axis.
spacing = 0.12
if config['xlim'] == [None, None] or len(config['xlim']) == 0:
maxvalue = df[['x', 'x1', 'x2']].max().max()
minvalue = df[['x', 'x1', 'x2']].min().min()
x_spacing = ((maxvalue - minvalue) * spacing)
config['xlim'] = [minvalue - x_spacing, maxvalue + x_spacing]
# x_spacing = (df['x'].max() - df['x'].min()) * spacing
# config['xlim'] = [df['x'].min() - x_spacing, df['x'].max() + x_spacing]
if config['ylim'] == [None, None] or len(config['ylim']) == 0:
maxvalue = df[['y', 'y1', 'y2']].max().max()
minvalue = df[['y', 'y1', 'y2']].min().min()
y_spacing = ((maxvalue - minvalue) * spacing)
config['ylim'] = [minvalue - y_spacing, maxvalue + y_spacing]
# y_spacing = (df['y'].max() - df['y'].min()) * spacing
# config['ylim'] = [df['y'].min() - y_spacing, df['y'].max() + y_spacing]
# Create the data from the input of javascript
X = get_data_ready_for_d3(df)
# Check whether tooltip is available. Otherwise remove the tooltip box.
if np.all(df['tooltip'] == ''):
config['mouseover'] = ''
config['mousemove'] = ''
config['mouseleave'] = ''
else:
config['mouseover'] = '.on("mouseover", mouseover)'
config['mousemove'] = '.on("mousemove", mousemove)'
config['mouseleave'] = '.on("mouseleave", mouseleave)'
# Write to HTML
return write_html(X, config, logger=logger)
def write_html(X, config, logger=None):
"""Write html.
Parameters
----------
X : list of str
Input data for javascript.
config : dict
Dictionary containing configuration keys.
Returns
-------
None.
"""
content = {
'json_data': X,
'COLOR_BACKGROUND': config['color_background'],
'TITLE': config['title'],
'WIDTH': config['figsize'][0],
'HEIGHT': config['figsize'][1],
'MIN_X': config['xlim'][0],
'MAX_X': config['xlim'][1],
'MIN_Y': config['ylim'][0],
'MAX_Y': config['ylim'][1],
'RADIO_LABEL1': config['label_radio'][0],
'RADIO_LABEL2': config['label_radio'][1],
'RADIO_LABEL3': config['label_radio'][2],
'RADIO_VISIBLE1': config['radio_button_visible'][0],
'RADIO_VISIBLE2': config['radio_button_visible'][1],
'RADIO_VISIBLE3': config['radio_button_visible'][2],
'MOUSEOVER': config['mouseover'],
'MOUSEMOVE': config['mousemove'],
'MOUSELEAVE': config['mouseleave'],
}
try:
jinja_env = Environment(loader=PackageLoader(package_name=__name__, package_path='d3js'))
except:
jinja_env = Environment(loader=PackageLoader(package_name='d3blocks.scatter', package_path='d3js'))
index_template = jinja_env.get_template('scatter.html.j2')
# index_file = Path(config['filepath'])
# # index_file.write_text(index_template.render(content))
# if config['overwrite'] and os.path.isfile(index_file):
# if logger is not None: logger.info('File already exists and will be overwritten: [%s]' %(index_file))
# os.remove(index_file)
# time.sleep(0.5)
# with open(index_file, "w", encoding="utf-8") as f:
# f.write(index_template.render(content))
# Generate html content
html = index_template.render(content)
write_html_file(config, html, logger)
# Return html
return html
def get_data_ready_for_d3(df):
"""Convert the source-target data into d3 compatible data.
Parameters
----------
df : pd.DataFrame()
Input data.
Returns
-------
X : str.
Converted data into a string that is d3 compatible.
"""
# Set x, y
X = df[['x', 'y', 'color', 'size', 'opacity', 'stroke', 'tooltip', 'x1', 'y1', 'x2', 'y2']].to_json(orient='values')
# Return
return X

17
实例学习d3blocks/官网例子/chart.py Normal file
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# Load d3blocks
from d3blocks import D3Blocks
#
# Initialize
d3 = D3Blocks(chart='Timeseries', frame=False)
#
# Import example
df = d3.import_example('climate')
#
# Node properties
d3.set_node_properties(df.columns.values)
d3.node_properties.get('wind_speed')['color']='#000000'
print(d3.node_properties)
#
d3.set_edge_properties(df, datetime='date', dt_format='%Y-%m-%d %H:%M:%S')
d3.show(title='Timeseries with adjusted configurations')

21
实例学习d3blocks/官网例子/imageslider.py Normal file
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# Load d3blocks
from d3blocks import D3Blocks
#
# Initialize
d3 = D3Blocks()
#
# Local images
img_before, img_after = d3.import_example('southern_nebula')
#
# Internet location
img_before, img_after = d3.import_example('southern_nebula_internet')
#
# Read the image in array
img_before = cv2.imread(img_before, -1)
img_after = cv2.imread(img_after, -1)
#
# Plot
d3.imageslider(img_before, img_after)
#
# Plot
d3.imageslider(img_before, img_after, showfig=True, scale=True, colorscale=2, figsize=[400, 400])

21
实例学习pandasai/helloworld.py Normal file
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import pandas as pd
from pandasai import PandasAI
# Sample DataFrame
df = pd.DataFrame({
"country": ["United States", "United Kingdom", "France", "Germany", "Italy", "Spain", "Canada", "Australia", "Japan", "China"],
"gdp": [19294482071552, 2891615567872, 2411255037952, 3435817336832, 1745433788416, 1181205135360, 1607402389504, 1490967855104, 4380756541440, 14631844184064],
"happiness_index": [6.94, 7.16, 6.66, 7.07, 6.38, 6.4, 7.23, 7.22, 5.87, 5.12]
})
# Instantiate a LLM
from pandasai.llm.openai import OpenAI
llm = OpenAI(api_token="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")
pandas_ai = PandasAI(llm)
pandas_ai.run(df, prompt='Which are the 5 happiest countries?')
pandas_ai.run(
df,
"Plot the histogram of countries showing for each the gpd, using different colors for each bar",
)

1
实例学习pandasai/readme Normal file
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@ -0,0 +1 @@
https://mp.weixin.qq.com/s/EKLipcw4tmwLbAo7lhdUig

21
实例学习streamlit/test.py Normal file
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import streamlit as st
import pandas as pd
from io import StringIO
uploaded_file = st.file_uploader("Choose a file")
if uploaded_file is not None:
# To read file as bytes:
bytes_data = uploaded_file.getvalue()
st.write(bytes_data)
# To convert to a string based IO:
stringio = StringIO(uploaded_file.getvalue().decode("utf-8"))
st.write(stringio)
# To read file as string:
string_data = stringio.read()
st.write(string_data)
# Can be used wherever a "file-like" object is accepted:
dataframe = pd.read_csv(uploaded_file)
st.write(dataframe)

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实验 探索Chipotle快餐数据/1.探索Chipotle快餐数据.py Normal file
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# -- 将数据集存入一个名为chipo的数据框内
# -- 查看前10行内容
# -- 数据集中有多少个列(columns)？
# -- 打印出全部的列名称
# -- 数据集的索引是怎样的？
# -- 被下单数最多商品(item)是什么?
# -- 在item_name这一列中，一共有多少种商品被下单？
# -- 在choice_description中，下单次数最多的商品是什么？
# -- 一共有多少商品被下单？
# -- 将item_price转换为浮点数
# -- 在该数据集对应的时期内，收入(revenue)是多少？
# -- 在该数据集对应的时期内，一共有多少订单？
# -- 每一单(order)对应的平均总价是多少？
import pandas as pd
#将数据集存入一个名为chipo的数据框内
chipo = pd.read_csv('data/chipotle.tsv',sep='\t')
print(chipo.head(10))
#数据集中有多少个列(columns)？
print(chipo.shape)
print(chipo.shape[1])
print(chipo.columns)
print(chipo.index)
#被下单数最多商品(item)是什么?
# print(chipo[['item_name','quantity']].groupby(by=['item_name']).describe())
a1 = chipo[['item_name','quantity']].groupby(by=['item_name']).sum().sort_values(by = ['quantity'],ascending=False)
print(a1)
#在item_name这一列中，一共有多少种商品被下单？
a2 = chipo['item_name'].nunique()
print(a2)
#在choice_description中，下单次数最多的商品是什么？
a3 = chipo[['quantity','choice_description']].groupby('choice_description').sum().sort_values('quantity',ascending=False)
a4 = chipo['choice_description'].value_counts().head()
print(a3,a4)
#一共有多少商品被下单？
a5 = chipo['quantity'].sum()
print(a5)
#将item_price转换为浮点数
#货币符号后取起
# print(chipo['item_price'])
#为什么从0开始不行？
#报错：could not convert string to float: '$2.39 '
a6 = chipo['item_price'] = chipo['item_price'].apply(lambda x:float(x[1:]))
#在该数据集对应的时期内，收入(revenue)是多少？
((chipo['quantity']*chipo['item_price'])).sum()
#在该数据集对应的时期内，一共有多少订单？
chipo['order_id'].nunique()
#每一单(order)对应的平均总价是多少？
chipo['item_price_sum'] = chipo['quantity'] * chipo['item_price']
chipo[['order_id','item_price_sum']].groupby('order_id').sum().mean()

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实验 探索Chipotle快餐数据/3.探索酒类消费数据.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"# -- 将数据框命名为drinks\n",
"# -- 哪个大陆(continent)平均消耗的啤酒(beer)更多？\n",
"# -- 打印出每个大陆(continent)的红酒消耗(wine_servings)的描述性统计值\n",
"# -- 打印出每个大陆每种酒类别的消耗平均值\n",
"# -- 打印出每个大陆每种酒类别的消耗中位数\n",
"# -- 打印出每个大陆对spirit饮品消耗的平均值，最大值和最小值"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"import pandas as pd\n",
"#将数据框命名为drinks\n",
"drinks = pd.read_csv('data/drinks.csv')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>beer_servings</th>\n",
" </tr>\n",
" <tr>\n",
" <th>continent</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>EU</th>\n",
" <td>193.777778</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" beer_servings\n",
"continent \n",
"EU 193.777778"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#哪个大陆(continent)平均消耗的啤酒(beer)更多？\n",
"drinks[['beer_servings','continent']].groupby('continent').mean().sort_values('beer_servings',ascending=False).head(1)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" <tr>\n",
" <th>continent</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AF</th>\n",
" <td>53.0</td>\n",
" <td>16.264151</td>\n",
" <td>38.846419</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>13.00</td>\n",
" <td>233.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AS</th>\n",
" <td>44.0</td>\n",
" <td>9.068182</td>\n",
" <td>21.667034</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>8.00</td>\n",
" <td>123.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>EU</th>\n",
" <td>45.0</td>\n",
" <td>142.222222</td>\n",
" <td>97.421738</td>\n",
" <td>0.0</td>\n",
" <td>59.0</td>\n",
" <td>128.0</td>\n",
" <td>195.00</td>\n",
" <td>370.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC</th>\n",
" <td>16.0</td>\n",
" <td>35.625000</td>\n",
" <td>64.555790</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>8.5</td>\n",
" <td>23.25</td>\n",
" <td>212.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SA</th>\n",
" <td>12.0</td>\n",
" <td>62.416667</td>\n",
" <td>88.620189</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>12.0</td>\n",
" <td>98.50</td>\n",
" <td>221.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 25% 50% 75% max\n",
"continent \n",
"AF 53.0 16.264151 38.846419 0.0 1.0 2.0 13.00 233.0\n",
"AS 44.0 9.068182 21.667034 0.0 0.0 1.0 8.00 123.0\n",
"EU 45.0 142.222222 97.421738 0.0 59.0 128.0 195.00 370.0\n",
"OC 16.0 35.625000 64.555790 0.0 1.0 8.5 23.25 212.0\n",
"SA 12.0 62.416667 88.620189 1.0 3.0 12.0 98.50 221.0"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# -- 打印出每个大陆(continent)的红酒消耗(wine_servings)的描述性统计值\n",
"# drinks[['wine_servings','continent']].groupby('continent').sum()\n",
"drinks.groupby('continent').wine_servings.describe()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>beer_servings</th>\n",
" <th>spirit_servings</th>\n",
" <th>wine_servings</th>\n",
" <th>total_litres_of_pure_alcohol</th>\n",
" </tr>\n",
" <tr>\n",
" <th>continent</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AF</th>\n",
" <td>61.471698</td>\n",
" <td>16.339623</td>\n",
" <td>16.264151</td>\n",
" <td>3.007547</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AS</th>\n",
" <td>37.045455</td>\n",
" <td>60.840909</td>\n",
" <td>9.068182</td>\n",
" <td>2.170455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>EU</th>\n",
" <td>193.777778</td>\n",
" <td>132.555556</td>\n",
" <td>142.222222</td>\n",
" <td>8.617778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC</th>\n",
" <td>89.687500</td>\n",
" <td>58.437500</td>\n",
" <td>35.625000</td>\n",
" <td>3.381250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SA</th>\n",
" <td>175.083333</td>\n",
" <td>114.750000</td>\n",
" <td>62.416667</td>\n",
" <td>6.308333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" beer_servings spirit_servings wine_servings \\\n",
"continent \n",
"AF 61.471698 16.339623 16.264151 \n",
"AS 37.045455 60.840909 9.068182 \n",
"EU 193.777778 132.555556 142.222222 \n",
"OC 89.687500 58.437500 35.625000 \n",
"SA 175.083333 114.750000 62.416667 \n",
"\n",
" total_litres_of_pure_alcohol \n",
"continent \n",
"AF 3.007547 \n",
"AS 2.170455 \n",
"EU 8.617778 \n",
"OC 3.381250 \n",
"SA 6.308333 "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# -- 打印出每个大陆每种酒类别的消耗平均值\n",
"# drinks\n",
"# drinks[['beer_servings','spirit_servings','wine_servings','continent']].groupby('continent').mean()\n",
"drinks.groupby('continent').mean()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>beer_servings</th>\n",
" <th>spirit_servings</th>\n",
" <th>wine_servings</th>\n",
" <th>total_litres_of_pure_alcohol</th>\n",
" </tr>\n",
" <tr>\n",
" <th>continent</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AF</th>\n",
" <td>32.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>2.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AS</th>\n",
" <td>17.5</td>\n",
" <td>16.0</td>\n",
" <td>1.0</td>\n",
" <td>1.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>EU</th>\n",
" <td>219.0</td>\n",
" <td>122.0</td>\n",
" <td>128.0</td>\n",
" <td>10.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC</th>\n",
" <td>52.5</td>\n",
" <td>37.0</td>\n",
" <td>8.5</td>\n",
" <td>1.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SA</th>\n",
" <td>162.5</td>\n",
" <td>108.5</td>\n",
" <td>12.0</td>\n",
" <td>6.85</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" beer_servings spirit_servings wine_servings \\\n",
"continent \n",
"AF 32.0 3.0 2.0 \n",
"AS 17.5 16.0 1.0 \n",
"EU 219.0 122.0 128.0 \n",
"OC 52.5 37.0 8.5 \n",
"SA 162.5 108.5 12.0 \n",
"\n",
" total_litres_of_pure_alcohol \n",
"continent \n",
"AF 2.30 \n",
"AS 1.20 \n",
"EU 10.00 \n",
"OC 1.75 \n",
"SA 6.85 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# -- 打印出每个大陆每种酒类别的消耗中位数\n",
"drinks.groupby('continent').median()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" <tr>\n",
" <th>continent</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AF</th>\n",
" <td>53.0</td>\n",
" <td>16.339623</td>\n",
" <td>28.102794</td>\n",
" <td>0.0</td>\n",
" <td>1.00</td>\n",
" <td>3.0</td>\n",
" <td>19.00</td>\n",
" <td>152.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AS</th>\n",
" <td>44.0</td>\n",
" <td>60.840909</td>\n",
" <td>84.362160</td>\n",
" <td>0.0</td>\n",
" <td>1.00</td>\n",
" <td>16.0</td>\n",
" <td>98.00</td>\n",
" <td>326.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>EU</th>\n",
" <td>45.0</td>\n",
" <td>132.555556</td>\n",
" <td>77.589115</td>\n",
" <td>0.0</td>\n",
" <td>81.00</td>\n",
" <td>122.0</td>\n",
" <td>173.00</td>\n",
" <td>373.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC</th>\n",
" <td>16.0</td>\n",
" <td>58.437500</td>\n",
" <td>70.504817</td>\n",
" <td>0.0</td>\n",
" <td>18.00</td>\n",
" <td>37.0</td>\n",
" <td>65.25</td>\n",
" <td>254.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SA</th>\n",
" <td>12.0</td>\n",
" <td>114.750000</td>\n",
" <td>77.077440</td>\n",
" <td>25.0</td>\n",
" <td>65.75</td>\n",
" <td>108.5</td>\n",
" <td>148.75</td>\n",
" <td>302.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 25% 50% 75% max\n",
"continent \n",
"AF 53.0 16.339623 28.102794 0.0 1.00 3.0 19.00 152.0\n",
"AS 44.0 60.840909 84.362160 0.0 1.00 16.0 98.00 326.0\n",
"EU 45.0 132.555556 77.589115 0.0 81.00 122.0 173.00 373.0\n",
"OC 16.0 58.437500 70.504817 0.0 18.00 37.0 65.25 254.0\n",
"SA 12.0 114.750000 77.077440 25.0 65.75 108.5 148.75 302.0"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#打印出每个大陆对spirit饮品消耗的平均值，最大值和最小值\n",
"drinks.groupby('continent').spirit_servings.describe()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# -- 将数据框命名为crime\n",
"# -- 每一列(column)的数据类型是什么样的？\n",
"# -- 将Year的数据类型转换为 datetime64\n",
"# -- 将列Year设置为数据框的索引\n",
"# -- 删除名为Total的列\n",
"# -- 按照Year（每十年）对数据框进行分组并求和\n",
"# -- 何时是美国历史上生存最危险的年代？"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"#将数据框命名为drinks\n",
"crime = pd.read_csv('data/US_Crime_Rates_1960_2014.csv',index_col=0)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 55 entries, 0 to 54\n",
"Data columns (total 12 columns):\n",
"Year 55 non-null int64\n",
"Population 55 non-null int64\n",
"Total 55 non-null int64\n",
"Violent 55 non-null int64\n",
"Property 55 non-null int64\n",
"Murder 55 non-null int64\n",
"Forcible_Rape 55 non-null int64\n",
"Robbery 55 non-null int64\n",
"Aggravated_assault 55 non-null int64\n",
"Burglary 55 non-null int64\n",
"Larceny_Theft 55 non-null int64\n",
"Vehicle_Theft 55 non-null int64\n",
"dtypes: int64(12)\n",
"memory usage: 5.6 KB\n"
]
}
],
"source": [
"#每一列(column)的数据类型是什么样的？\n",
"crime.info()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"</style>\n",
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" <th></th>\n",
" <th>Population</th>\n",
" <th>Total</th>\n",
" <th>Violent</th>\n",
" <th>Property</th>\n",
" <th>Murder</th>\n",
" <th>Forcible_Rape</th>\n",
" <th>Robbery</th>\n",
" <th>Aggravated_assault</th>\n",
" <th>Burglary</th>\n",
" <th>Larceny_Theft</th>\n",
" <th>Vehicle_Theft</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1960-01-01</th>\n",
" <td>179323175</td>\n",
" <td>3384200</td>\n",
" <td>288460</td>\n",
" <td>3095700</td>\n",
" <td>9110</td>\n",
" <td>17190</td>\n",
" <td>107840</td>\n",
" <td>154320</td>\n",
" <td>912100</td>\n",
" <td>1855400</td>\n",
" <td>328200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1961-01-01</th>\n",
" <td>182992000</td>\n",
" <td>3488000</td>\n",
" <td>289390</td>\n",
" <td>3198600</td>\n",
" <td>8740</td>\n",
" <td>17220</td>\n",
" <td>106670</td>\n",
" <td>156760</td>\n",
" <td>949600</td>\n",
" <td>1913000</td>\n",
" <td>336000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1962-01-01</th>\n",
" <td>185771000</td>\n",
" <td>3752200</td>\n",
" <td>301510</td>\n",
" <td>3450700</td>\n",
" <td>8530</td>\n",
" <td>17550</td>\n",
" <td>110860</td>\n",
" <td>164570</td>\n",
" <td>994300</td>\n",
" <td>2089600</td>\n",
" <td>366800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1963-01-01</th>\n",
" <td>188483000</td>\n",
" <td>4109500</td>\n",
" <td>316970</td>\n",
" <td>3792500</td>\n",
" <td>8640</td>\n",
" <td>17650</td>\n",
" <td>116470</td>\n",
" <td>174210</td>\n",
" <td>1086400</td>\n",
" <td>2297800</td>\n",
" <td>408300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1964-01-01</th>\n",
" <td>191141000</td>\n",
" <td>4564600</td>\n",
" <td>364220</td>\n",
" <td>4200400</td>\n",
" <td>9360</td>\n",
" <td>21420</td>\n",
" <td>130390</td>\n",
" <td>203050</td>\n",
" <td>1213200</td>\n",
" <td>2514400</td>\n",
" <td>472800</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Population Total Violent Property Murder Forcible_Rape \\\n",
"Year \n",
"1960-01-01 179323175 3384200 288460 3095700 9110 17190 \n",
"1961-01-01 182992000 3488000 289390 3198600 8740 17220 \n",
"1962-01-01 185771000 3752200 301510 3450700 8530 17550 \n",
"1963-01-01 188483000 4109500 316970 3792500 8640 17650 \n",
"1964-01-01 191141000 4564600 364220 4200400 9360 21420 \n",
"\n",
" Robbery Aggravated_assault Burglary Larceny_Theft \\\n",
"Year \n",
"1960-01-01 107840 154320 912100 1855400 \n",
"1961-01-01 106670 156760 949600 1913000 \n",
"1962-01-01 110860 164570 994300 2089600 \n",
"1963-01-01 116470 174210 1086400 2297800 \n",
"1964-01-01 130390 203050 1213200 2514400 \n",
"\n",
" Vehicle_Theft \n",
"Year \n",
"1960-01-01 328200 \n",
"1961-01-01 336000 \n",
"1962-01-01 366800 \n",
"1963-01-01 408300 \n",
"1964-01-01 472800 "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#将Year的数据类型转换为 datetime64\n",
"crime.Year = pd.to_datetime(crime.Year,format='%Y')\n",
"#将列Year设置为数据框的索引\n",
"crime = crime.set_index('Year',drop=True)\n",
"crime.head()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Population</th>\n",
" <th>Violent</th>\n",
" <th>Property</th>\n",
" <th>Murder</th>\n",
" <th>Forcible_Rape</th>\n",
" <th>Robbery</th>\n",
" <th>Aggravated_assault</th>\n",
" <th>Burglary</th>\n",
" <th>Larceny_Theft</th>\n",
" <th>Vehicle_Theft</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1960-01-01</th>\n",
" <td>179323175</td>\n",
" <td>288460</td>\n",
" <td>3095700</td>\n",
" <td>9110</td>\n",
" <td>17190</td>\n",
" <td>107840</td>\n",
" <td>154320</td>\n",
" <td>912100</td>\n",
" <td>1855400</td>\n",
" <td>328200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1961-01-01</th>\n",
" <td>182992000</td>\n",
" <td>289390</td>\n",
" <td>3198600</td>\n",
" <td>8740</td>\n",
" <td>17220</td>\n",
" <td>106670</td>\n",
" <td>156760</td>\n",
" <td>949600</td>\n",
" <td>1913000</td>\n",
" <td>336000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1962-01-01</th>\n",
" <td>185771000</td>\n",
" <td>301510</td>\n",
" <td>3450700</td>\n",
" <td>8530</td>\n",
" <td>17550</td>\n",
" <td>110860</td>\n",
" <td>164570</td>\n",
" <td>994300</td>\n",
" <td>2089600</td>\n",
" <td>366800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1963-01-01</th>\n",
" <td>188483000</td>\n",
" <td>316970</td>\n",
" <td>3792500</td>\n",
" <td>8640</td>\n",
" <td>17650</td>\n",
" <td>116470</td>\n",
" <td>174210</td>\n",
" <td>1086400</td>\n",
" <td>2297800</td>\n",
" <td>408300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1964-01-01</th>\n",
" <td>191141000</td>\n",
" <td>364220</td>\n",
" <td>4200400</td>\n",
" <td>9360</td>\n",
" <td>21420</td>\n",
" <td>130390</td>\n",
" <td>203050</td>\n",
" <td>1213200</td>\n",
" <td>2514400</td>\n",
" <td>472800</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Population Violent Property Murder Forcible_Rape Robbery \\\n",
"Year \n",
"1960-01-01 179323175 288460 3095700 9110 17190 107840 \n",
"1961-01-01 182992000 289390 3198600 8740 17220 106670 \n",
"1962-01-01 185771000 301510 3450700 8530 17550 110860 \n",
"1963-01-01 188483000 316970 3792500 8640 17650 116470 \n",
"1964-01-01 191141000 364220 4200400 9360 21420 130390 \n",
"\n",
" Aggravated_assault Burglary Larceny_Theft Vehicle_Theft \n",
"Year \n",
"1960-01-01 154320 912100 1855400 328200 \n",
"1961-01-01 156760 949600 1913000 336000 \n",
"1962-01-01 164570 994300 2089600 366800 \n",
"1963-01-01 174210 1086400 2297800 408300 \n",
"1964-01-01 203050 1213200 2514400 472800 "
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#删除名为Total的列\n",
"del crime['Total']\n",
"crime.head()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <th>Murder</th>\n",
" <th>Forcible_Rape</th>\n",
" <th>Robbery</th>\n",
" <th>Aggravated_assault</th>\n",
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" <th>Year</th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>1960-01-01</th>\n",
" <td>1915053175</td>\n",
" <td>4134930</td>\n",
" <td>45160900</td>\n",
" <td>106180</td>\n",
" <td>236720</td>\n",
" <td>1633510</td>\n",
" <td>2158520</td>\n",
" <td>13321100</td>\n",
" <td>26547700</td>\n",
" <td>5292100</td>\n",
" <td>&lt;bound method f of DatetimeIndexResampler [fre...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1970-01-01</th>\n",
" <td>2121193298</td>\n",
" <td>9607930</td>\n",
" <td>91383800</td>\n",
" <td>192230</td>\n",
" <td>554570</td>\n",
" <td>4159020</td>\n",
" <td>4702120</td>\n",
" <td>28486000</td>\n",
" <td>53157800</td>\n",
" <td>9739900</td>\n",
" <td>&lt;bound method f of DatetimeIndexResampler [fre...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1980-01-01</th>\n",
" <td>2371370069</td>\n",
" <td>14074328</td>\n",
" <td>117048900</td>\n",
" <td>206439</td>\n",
" <td>865639</td>\n",
" <td>5383109</td>\n",
" <td>7619130</td>\n",
" <td>33073494</td>\n",
" <td>72040253</td>\n",
" <td>11935411</td>\n",
" <td>&lt;bound method f of DatetimeIndexResampler [fre...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1990-01-01</th>\n",
" <td>2612825258</td>\n",
" <td>17527048</td>\n",
" <td>119053499</td>\n",
" <td>211664</td>\n",
" <td>998827</td>\n",
" <td>5748930</td>\n",
" <td>10568963</td>\n",
" <td>26750015</td>\n",
" <td>77679366</td>\n",
" <td>14624418</td>\n",
" <td>&lt;bound method f of DatetimeIndexResampler [fre...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2000-01-01</th>\n",
" <td>2947969117</td>\n",
" <td>13968056</td>\n",
" <td>100944369</td>\n",
" <td>163068</td>\n",
" <td>922499</td>\n",
" <td>4230366</td>\n",
" <td>8652124</td>\n",
" <td>21565176</td>\n",
" <td>67970291</td>\n",
" <td>11412834</td>\n",
" <td>&lt;bound method f of DatetimeIndexResampler [fre...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Population Violent Property Murder Forcible_Rape Robbery \\\n",
"Year \n",
"1960-01-01 1915053175 4134930 45160900 106180 236720 1633510 \n",
"1970-01-01 2121193298 9607930 91383800 192230 554570 4159020 \n",
"1980-01-01 2371370069 14074328 117048900 206439 865639 5383109 \n",
"1990-01-01 2612825258 17527048 119053499 211664 998827 5748930 \n",
"2000-01-01 2947969117 13968056 100944369 163068 922499 4230366 \n",
"\n",
" Aggravated_assault Burglary Larceny_Theft Vehicle_Theft \\\n",
"Year \n",
"1960-01-01 2158520 13321100 26547700 5292100 \n",
"1970-01-01 4702120 28486000 53157800 9739900 \n",
"1980-01-01 7619130 33073494 72040253 11935411 \n",
"1990-01-01 10568963 26750015 77679366 14624418 \n",
"2000-01-01 8652124 21565176 67970291 11412834 \n",
"\n",
" population \n",
"Year \n",
"1960-01-01 <bound method f of DatetimeIndexResampler [fre... \n",
"1970-01-01 <bound method f of DatetimeIndexResampler [fre... \n",
"1980-01-01 <bound method f of DatetimeIndexResampler [fre... \n",
"1990-01-01 <bound method f of DatetimeIndexResampler [fre... \n",
"2000-01-01 <bound method f of DatetimeIndexResampler [fre... "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#按照Year（每十年）对数据框进行分组并求和\n",
"#按时间聚合参考博客：https://www.jb51.net/article/177325.htm\n",
"crimes = crime.resample('10AS').sum()\n",
"#人口是累计数，不能直接求和\n",
"crimes['population'] = crime.resample('10AS').max\n",
"crimes.head()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Population 2014-01-01\n",
"Violent 1992-01-01\n",
"Property 1991-01-01\n",
"Murder 1991-01-01\n",
"Forcible_Rape 1992-01-01\n",
"Robbery 1991-01-01\n",
"Aggravated_assault 1993-01-01\n",
"Burglary 1980-01-01\n",
"Larceny_Theft 1991-01-01\n",
"Vehicle_Theft 1991-01-01\n",
"dtype: datetime64[ns]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#何时是美国历史上生存最危险的年代？\n",
"crime.idxmax()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
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{
"cell_type": "code",
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809
实验 探索Chipotle快餐数据/5.探索虚拟姓名数据.ipynb Normal file
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@ -0,0 +1,809 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# -- 创建DataFrame\n",
"# -- 将上述的DataFrame分别命名为data1, data2, data3\n",
"# -- 将data1和data2两个数据框按照行的维度进行合并，命名为all_data\n",
"# -- 将data1和data2两个数据框按照列的维度进行合并，命名为all_data_col\n",
"# -- 打印data3\n",
"# -- 按照subject_id的值对all_data和data3作合并\n",
"# -- 对data1和data2按照subject_id作连接\n",
"# -- 找到 data1 和 data2 合并之后的所有匹配结果"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"raw_data_1 = {\n",
" 'subject_id': ['1', '2', '3', '4', '5'],\n",
" 'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'], \n",
" 'last_name': ['Anderson', 'Ackerman', 'Ali', 'Aoni', 'Atiches']}\n",
"\n",
"raw_data_2 = {\n",
" 'subject_id': ['4', '5', '6', '7', '8'],\n",
" 'first_name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'], \n",
" 'last_name': ['Bonder', 'Black', 'Balwner', 'Brice', 'Btisan']}\n",
"\n",
"raw_data_3 = {\n",
" 'subject_id': ['1', '2', '3', '4', '5', '7', '8', '9', '10', '11'],\n",
" 'test_id': [51, 15, 15, 61, 16, 14, 15, 1, 61, 16]}"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>subject_id</th>\n",
" <th>first_name</th>\n",
" <th>last_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Alex</td>\n",
" <td>Anderson</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
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" <td>Ackerman</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>Allen</td>\n",
" <td>Ali</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>Alice</td>\n",
" <td>Aoni</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Ayoung</td>\n",
" <td>Atiches</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
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"text/plain": [
" subject_id first_name last_name\n",
"0 1 Alex Anderson\n",
"1 2 Amy Ackerman\n",
"2 3 Allen Ali\n",
"3 4 Alice Aoni\n",
"4 5 Ayoung Atiches"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#将上述的DataFrame分别命名为data1, data2, data3\n",
"data1 = pd.DataFrame(raw_data_1)\n",
"data2 = pd.DataFrame(raw_data_2)\n",
"data3 = pd.DataFrame(raw_data_3)\n",
"data1"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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" <tr>\n",
" <th>1</th>\n",
" <td>5</td>\n",
" <td>Brian</td>\n",
" <td>Black</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>6</td>\n",
" <td>Bran</td>\n",
" <td>Balwner</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>7</td>\n",
" <td>Bryce</td>\n",
" <td>Brice</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>8</td>\n",
" <td>Betty</td>\n",
" <td>Btisan</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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"text/plain": [
" subject_id first_name last_name\n",
"0 1 Alex Anderson\n",
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"2 3 Allen Ali\n",
"3 4 Alice Aoni\n",
"4 5 Ayoung Atiches\n",
"0 4 Billy Bonder\n",
"1 5 Brian Black\n",
"2 6 Bran Balwner\n",
"3 7 Bryce Brice\n",
"4 8 Betty Btisan"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#将data1和data2两个数据框按照行的维度进行合并，命名为all_data\n",
"all_data = pd.concat([data1,data2],axis=0)\n",
"all_data"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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" <td>6</td>\n",
" <td>Bran</td>\n",
" <td>Balwner</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>Alice</td>\n",
" <td>Aoni</td>\n",
" <td>7</td>\n",
" <td>Bryce</td>\n",
" <td>Brice</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Ayoung</td>\n",
" <td>Atiches</td>\n",
" <td>8</td>\n",
" <td>Betty</td>\n",
" <td>Btisan</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" subject_id first_name last_name subject_id first_name last_name\n",
"0 1 Alex Anderson 4 Billy Bonder\n",
"1 2 Amy Ackerman 5 Brian Black\n",
"2 3 Allen Ali 6 Bran Balwner\n",
"3 4 Alice Aoni 7 Bryce Brice\n",
"4 5 Ayoung Atiches 8 Betty Btisan"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#将data1和data2两个数据框按照列的维度进行合并，命名为all_data_col\n",
"all_data_col = pd.concat([data1,data2],axis=1)\n",
"all_data_col"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"text/plain": [
" subject_id test_id\n",
"0 1 51\n",
"1 2 15\n",
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"3 4 61\n",
"4 5 16\n",
"5 7 14\n",
"6 8 15\n",
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"9 11 16"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data3"
]
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{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
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" <td>Alice</td>\n",
" <td>Aoni</td>\n",
" <td>61</td>\n",
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" <tr>\n",
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" <td>61</td>\n",
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" <tr>\n",
" <th>5</th>\n",
" <td>5</td>\n",
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" <td>Atiches</td>\n",
" <td>16</td>\n",
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" <tr>\n",
" <th>6</th>\n",
" <td>5</td>\n",
" <td>Brian</td>\n",
" <td>Black</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7</td>\n",
" <td>Bryce</td>\n",
" <td>Brice</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>8</td>\n",
" <td>Betty</td>\n",
" <td>Btisan</td>\n",
" <td>15</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" subject_id first_name last_name test_id\n",
"0 1 Alex Anderson 51\n",
"1 2 Amy Ackerman 15\n",
"2 3 Allen Ali 15\n",
"3 4 Alice Aoni 61\n",
"4 4 Billy Bonder 61\n",
"5 5 Ayoung Atiches 16\n",
"6 5 Brian Black 16\n",
"7 7 Bryce Brice 14\n",
"8 8 Betty Btisan 15"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#按照subject_id的值对all_data和data3作合并\n",
"pd.merge(all_data,data3,on='subject_id')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" <th>1</th>\n",
" <td>5</td>\n",
" <td>Ayoung</td>\n",
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" <td>Brian</td>\n",
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" </tr>\n",
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"</table>\n",
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],
"text/plain": [
" subject_id first_name_x last_name_x first_name_y last_name_y\n",
"0 4 Alice Aoni Billy Bonder\n",
"1 5 Ayoung Atiches Brian Black"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#对data1和data2按照subject_id作内连接\n",
"pd.merge(data1,data2,on='subject_id',how='inner')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" <th>last_name_data2</th>\n",
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" <tr>\n",
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" <td>2</td>\n",
" <td>Amy</td>\n",
" <td>Ackerman</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>Allen</td>\n",
" <td>Ali</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>Alice</td>\n",
" <td>Aoni</td>\n",
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" <tr>\n",
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" <td>Ayoung</td>\n",
" <td>Atiches</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Bran</td>\n",
" <td>Balwner</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Bryce</td>\n",
" <td>Brice</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Betty</td>\n",
" <td>Btisan</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" subject_id first_name_data1 last_name_data1 first_name_data2 last_name_data2\n",
"0 1 Alex Anderson NaN NaN\n",
"1 2 Amy Ackerman NaN NaN\n",
"2 3 Allen Ali NaN NaN\n",
"3 4 Alice Aoni Billy Bonder\n",
"4 5 Ayoung Atiches Brian Black\n",
"5 6 NaN NaN Bran Balwner\n",
"6 7 NaN NaN Bryce Brice\n",
"7 8 NaN NaN Betty Btisan"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#找到 data1 和 data2 合并之后的所有匹配结果\n",
"pd.merge(data1,data2,on='subject_id',how='outer',suffixes=('_data1', '_data2'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"# -- 读取数据并存为一个名叫apple的数据框\n",
"# -- 查看每一列的数据类型\n",
"# -- 将Date这个列转换为datetime类型\n",
"# -- 将Date设置为索引\n",
"# -- 有重复的日期吗？\n",
"# -- 将index设置为升序\n",
"# -- 找到每个月的最后一个交易日(business day)\n",
"# -- 数据集中最早的日期和最晚的日期相差多少天？\n",
"# -- 在数据中一共有多少个月？\n",
"# -- 按照时间顺序可视化Adj Close值"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"import pandas as pd\n",
"#读取数据并存为一个名叫apple的数据框\n",
"apple = pd.read_csv('data/appl_1980_2014.csv')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Date object\n",
"Open float64\n",
"High float64\n",
"Low float64\n",
"Close float64\n",
"Volume int64\n",
"Adj Close float64\n",
"dtype: object"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#查看每一列的数据类型\n",
"apple.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Adj Close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2014-07-08</td>\n",
" <td>96.27</td>\n",
" <td>96.80</td>\n",
" <td>93.92</td>\n",
" <td>95.35</td>\n",
" <td>65130000</td>\n",
" <td>95.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2014-07-07</td>\n",
" <td>94.14</td>\n",
" <td>95.99</td>\n",
" <td>94.10</td>\n",
" <td>95.97</td>\n",
" <td>56305400</td>\n",
" <td>95.97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014-07-03</td>\n",
" <td>93.67</td>\n",
" <td>94.10</td>\n",
" <td>93.20</td>\n",
" <td>94.03</td>\n",
" <td>22891800</td>\n",
" <td>94.03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2014-07-02</td>\n",
" <td>93.87</td>\n",
" <td>94.06</td>\n",
" <td>93.09</td>\n",
" <td>93.48</td>\n",
" <td>28420900</td>\n",
" <td>93.48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2014-07-01</td>\n",
" <td>93.52</td>\n",
" <td>94.07</td>\n",
" <td>93.13</td>\n",
" <td>93.52</td>\n",
" <td>38170200</td>\n",
" <td>93.52</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close Volume Adj Close\n",
"0 2014-07-08 96.27 96.80 93.92 95.35 65130000 95.35\n",
"1 2014-07-07 94.14 95.99 94.10 95.97 56305400 95.97\n",
"2 2014-07-03 93.67 94.10 93.20 94.03 22891800 94.03\n",
"3 2014-07-02 93.87 94.06 93.09 93.48 28420900 93.48\n",
"4 2014-07-01 93.52 94.07 93.13 93.52 38170200 93.52"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"apple.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Adj Close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2014-07-08</td>\n",
" <td>96.27</td>\n",
" <td>96.80</td>\n",
" <td>93.92</td>\n",
" <td>95.35</td>\n",
" <td>65130000</td>\n",
" <td>95.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2014-07-07</td>\n",
" <td>94.14</td>\n",
" <td>95.99</td>\n",
" <td>94.10</td>\n",
" <td>95.97</td>\n",
" <td>56305400</td>\n",
" <td>95.97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014-07-03</td>\n",
" <td>93.67</td>\n",
" <td>94.10</td>\n",
" <td>93.20</td>\n",
" <td>94.03</td>\n",
" <td>22891800</td>\n",
" <td>94.03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2014-07-02</td>\n",
" <td>93.87</td>\n",
" <td>94.06</td>\n",
" <td>93.09</td>\n",
" <td>93.48</td>\n",
" <td>28420900</td>\n",
" <td>93.48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2014-07-01</td>\n",
" <td>93.52</td>\n",
" <td>94.07</td>\n",
" <td>93.13</td>\n",
" <td>93.52</td>\n",
" <td>38170200</td>\n",
" <td>93.52</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close Volume Adj Close\n",
"0 2014-07-08 96.27 96.80 93.92 95.35 65130000 95.35\n",
"1 2014-07-07 94.14 95.99 94.10 95.97 56305400 95.97\n",
"2 2014-07-03 93.67 94.10 93.20 94.03 22891800 94.03\n",
"3 2014-07-02 93.87 94.06 93.09 93.48 28420900 93.48\n",
"4 2014-07-01 93.52 94.07 93.13 93.52 38170200 93.52"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"apple['Date'] = pd.to_datetime(apple['Date'])\n",
"apple.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Adj Close</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2014-07-08</th>\n",
" <td>96.27</td>\n",
" <td>96.80</td>\n",
" <td>93.92</td>\n",
" <td>95.35</td>\n",
" <td>65130000</td>\n",
" <td>95.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-07-07</th>\n",
" <td>94.14</td>\n",
" <td>95.99</td>\n",
" <td>94.10</td>\n",
" <td>95.97</td>\n",
" <td>56305400</td>\n",
" <td>95.97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-07-03</th>\n",
" <td>93.67</td>\n",
" <td>94.10</td>\n",
" <td>93.20</td>\n",
" <td>94.03</td>\n",
" <td>22891800</td>\n",
" <td>94.03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-07-02</th>\n",
" <td>93.87</td>\n",
" <td>94.06</td>\n",
" <td>93.09</td>\n",
" <td>93.48</td>\n",
" <td>28420900</td>\n",
" <td>93.48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-07-01</th>\n",
" <td>93.52</td>\n",
" <td>94.07</td>\n",
" <td>93.13</td>\n",
" <td>93.52</td>\n",
" <td>38170200</td>\n",
" <td>93.52</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Open High Low Close Volume Adj Close\n",
"Date \n",
"2014-07-08 96.27 96.80 93.92 95.35 65130000 95.35\n",
"2014-07-07 94.14 95.99 94.10 95.97 56305400 95.97\n",
"2014-07-03 93.67 94.10 93.20 94.03 22891800 94.03\n",
"2014-07-02 93.87 94.06 93.09 93.48 28420900 93.48\n",
"2014-07-01 93.52 94.07 93.13 93.52 38170200 93.52"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#将Date设置为索引\n",
"apple = apple.set_index('Date')\n",
"apple.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#有重复的日期吗？\n",
"apple.index.is_unique"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Adj Close</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1980-12-12</th>\n",
" <td>28.75</td>\n",
" <td>28.87</td>\n",
" <td>28.75</td>\n",
" <td>28.75</td>\n",
" <td>117258400</td>\n",
" <td>0.45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1980-12-15</th>\n",
" <td>27.38</td>\n",
" <td>27.38</td>\n",
" <td>27.25</td>\n",
" <td>27.25</td>\n",
" <td>43971200</td>\n",
" <td>0.42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1980-12-16</th>\n",
" <td>25.37</td>\n",
" <td>25.37</td>\n",
" <td>25.25</td>\n",
" <td>25.25</td>\n",
" <td>26432000</td>\n",
" <td>0.39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1980-12-17</th>\n",
" <td>25.87</td>\n",
" <td>26.00</td>\n",
" <td>25.87</td>\n",
" <td>25.87</td>\n",
" <td>21610400</td>\n",
" <td>0.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1980-12-18</th>\n",
" <td>26.63</td>\n",
" <td>26.75</td>\n",
" <td>26.63</td>\n",
" <td>26.63</td>\n",
" <td>18362400</td>\n",
" <td>0.41</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Open High Low Close Volume Adj Close\n",
"Date \n",
"1980-12-12 28.75 28.87 28.75 28.75 117258400 0.45\n",
"1980-12-15 27.38 27.38 27.25 27.25 43971200 0.42\n",
"1980-12-16 25.37 25.37 25.25 25.25 26432000 0.39\n",
"1980-12-17 25.87 26.00 25.87 25.87 21610400 0.40\n",
"1980-12-18 26.63 26.75 26.63 26.63 18362400 0.41"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#将index设置为升序\n",
"apple = apple.sort_index(ascending=True)\n",
"apple.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Adj Close</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1980-12-31</th>\n",
" <td>30.481538</td>\n",
" <td>30.567692</td>\n",
" <td>30.443077</td>\n",
" <td>30.443077</td>\n",
" <td>2.586252e+07</td>\n",
" <td>0.473077</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1981-01-30</th>\n",
" <td>31.754762</td>\n",
" <td>31.826667</td>\n",
" <td>31.654762</td>\n",
" <td>31.654762</td>\n",
" <td>7.249867e+06</td>\n",
" <td>0.493810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1981-02-27</th>\n",
" <td>26.480000</td>\n",
" <td>26.572105</td>\n",
" <td>26.407895</td>\n",
" <td>26.407895</td>\n",
" <td>4.231832e+06</td>\n",
" <td>0.411053</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1981-03-31</th>\n",
" <td>24.937727</td>\n",
" <td>25.016818</td>\n",
" <td>24.836364</td>\n",
" <td>24.836364</td>\n",
" <td>7.962691e+06</td>\n",
" <td>0.387727</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1981-04-30</th>\n",
" <td>27.286667</td>\n",
" <td>27.368095</td>\n",
" <td>27.227143</td>\n",
" <td>27.227143</td>\n",
" <td>6.392000e+06</td>\n",
" <td>0.423333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Open High Low Close Volume \\\n",
"Date \n",
"1980-12-31 30.481538 30.567692 30.443077 30.443077 2.586252e+07 \n",
"1981-01-30 31.754762 31.826667 31.654762 31.654762 7.249867e+06 \n",
"1981-02-27 26.480000 26.572105 26.407895 26.407895 4.231832e+06 \n",
"1981-03-31 24.937727 25.016818 24.836364 24.836364 7.962691e+06 \n",
"1981-04-30 27.286667 27.368095 27.227143 27.227143 6.392000e+06 \n",
"\n",
" Adj Close \n",
"Date \n",
"1980-12-31 0.473077 \n",
"1981-01-30 0.493810 \n",
"1981-02-27 0.411053 \n",
"1981-03-31 0.387727 \n",
"1981-04-30 0.423333 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#找到每个月的最后一个交易日(business day)\n",
"#resample参数详解：https://www.jianshu.com/p/5367ef7453ce\n",
"apple_month = apple.resample('BM').mean()\n",
"apple_month.head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/plain": [
"12261"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#数据集中最早的日期和最晚的日期相差多少天？\n",
"(apple.index.max() - apple.index.min()).days"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [
{
"data": {
"text/plain": [
"404"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(apple_month)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

17
实验 探索Chipotle快餐数据/data/Euro2012.csv Normal file
View File

@ -0,0 +1,17 @@
Team,Goals,Shots on target,Shots off target,Shooting Accuracy,% Goals-to-shots,Total shots (inc. Blocked),Hit Woodwork,Penalty goals,Penalties not scored,Headed goals,Passes,Passes completed,Passing Accuracy,Touches,Crosses,Dribbles,Corners Taken,Tackles,Clearances,Interceptions,Clearances off line,Clean Sheets,Blocks,Goals conceded,Saves made,Saves-to-shots ratio,Fouls Won,Fouls Conceded,Offsides,Yellow Cards,Red Cards,Subs on,Subs off,Players Used
Croatia,4,13,12,51.9%,16.0%,32,0,0,0,2,1076,828,76.9%,1706,60,42,14,49,83,56,,0,10,3,13,81.3%,41,62,2,9,0,9,9,16
Czech Republic,4,13,18,41.9%,12.9%,39,0,0,0,0,1565,1223,78.1%,2358,46,68,21,62,98,37,2.0,1,10,6,9,60.1%,53,73,8,7,0,11,11,19
Denmark,4,10,10,50.0%,20.0%,27,1,0,0,3,1298,1082,83.3%,1873,43,32,16,40,61,59,0.0,1,10,5,10,66.7%,25,38,8,4,0,7,7,15
England,5,11,18,50.0%,17.2%,40,0,0,0,3,1488,1200,80.6%,2440,58,60,16,86,106,72,1.0,2,29,3,22,88.1%,43,45,6,5,0,11,11,16
France,3,22,24,37.9%,6.5%,65,1,0,0,0,2066,1803,87.2%,2909,55,76,28,71,76,58,0.0,1,7,5,6,54.6%,36,51,5,6,0,11,11,19
Germany,10,32,32,47.8%,15.6%,80,2,1,0,2,2774,2427,87.4%,3761,101,60,35,91,73,69,0.0,1,11,6,10,62.6%,63,49,12,4,0,15,15,17
Greece,5,8,18,30.7%,19.2%,32,1,1,1,0,1187,911,76.7%,2016,52,53,10,65,123,87,0.0,1,23,7,13,65.1%,67,48,12,9,1,12,12,20
Italy,6,34,45,43.0%,7.5%,110,2,0,0,2,3016,2531,83.9%,4363,75,75,30,98,137,136,1.0,2,18,7,20,74.1%,101,89,16,16,0,18,18,19
Netherlands,2,12,36,25.0%,4.1%,60,2,0,0,0,1556,1381,88.7%,2163,50,49,22,34,41,41,0.0,0,9,5,12,70.6%,35,30,3,5,0,7,7,15
Poland,2,15,23,39.4%,5.2%,48,0,0,0,1,1059,852,80.4%,1724,55,39,14,67,87,62,0.0,0,8,3,6,66.7%,48,56,3,7,1,7,7,17
Portugal,6,22,42,34.3%,9.3%,82,6,0,0,2,1891,1461,77.2%,2958,91,64,41,78,92,86,0.0,2,11,4,10,71.5%,73,90,10,12,0,14,14,16
Republic of Ireland,1,7,12,36.8%,5.2%,28,0,0,0,1,851,606,71.2%,1433,43,18,8,45,78,43,1.0,0,23,9,17,65.4%,43,51,11,6,1,10,10,17
Russia,5,9,31,22.5%,12.5%,59,2,0,0,1,1602,1345,83.9%,2278,40,40,21,65,74,58,0.0,0,8,3,10,77.0%,34,43,4,6,0,7,7,16
Spain,12,42,33,55.9%,16.0%,100,0,1,0,2,4317,3820,88.4%,5585,69,106,44,122,102,79,0.0,5,8,1,15,93.8%,102,83,19,11,0,17,17,18
Sweden,5,17,19,47.2%,13.8%,39,3,0,0,1,1192,965,80.9%,1806,44,29,7,56,54,45,0.0,1,12,5,8,61.6%,35,51,7,7,0,9,9,18
Ukraine,2,7,26,21.2%,6.0%,38,0,0,0,2,1276,1043,81.7%,1894,33,26,18,65,97,29,0.0,0,4,4,13,76.5%,48,31,4,5,0,9,9,18
1 Team Goals Shots on target Shots off target Shooting Accuracy % Goals-to-shots Total shots (inc. Blocked) Hit Woodwork Penalty goals Penalties not scored Headed goals Passes Passes completed Passing Accuracy Touches Crosses Dribbles Corners Taken Tackles Clearances Interceptions Clearances off line Clean Sheets Blocks Goals conceded Saves made Saves-to-shots ratio Fouls Won Fouls Conceded Offsides Yellow Cards Red Cards Subs on Subs off Players Used
2 Croatia 4 13 12 51.9% 16.0% 32 0 0 0 2 1076 828 76.9% 1706 60 42 14 49 83 56 0 10 3 13 81.3% 41 62 2 9 0 9 9 16
3 Czech Republic 4 13 18 41.9% 12.9% 39 0 0 0 0 1565 1223 78.1% 2358 46 68 21 62 98 37 2.0 1 10 6 9 60.1% 53 73 8 7 0 11 11 19
4 Denmark 4 10 10 50.0% 20.0% 27 1 0 0 3 1298 1082 83.3% 1873 43 32 16 40 61 59 0.0 1 10 5 10 66.7% 25 38 8 4 0 7 7 15
5 England 5 11 18 50.0% 17.2% 40 0 0 0 3 1488 1200 80.6% 2440 58 60 16 86 106 72 1.0 2 29 3 22 88.1% 43 45 6 5 0 11 11 16
6 France 3 22 24 37.9% 6.5% 65 1 0 0 0 2066 1803 87.2% 2909 55 76 28 71 76 58 0.0 1 7 5 6 54.6% 36 51 5 6 0 11 11 19
7 Germany 10 32 32 47.8% 15.6% 80 2 1 0 2 2774 2427 87.4% 3761 101 60 35 91 73 69 0.0 1 11 6 10 62.6% 63 49 12 4 0 15 15 17
8 Greece 5 8 18 30.7% 19.2% 32 1 1 1 0 1187 911 76.7% 2016 52 53 10 65 123 87 0.0 1 23 7 13 65.1% 67 48 12 9 1 12 12 20
9 Italy 6 34 45 43.0% 7.5% 110 2 0 0 2 3016 2531 83.9% 4363 75 75 30 98 137 136 1.0 2 18 7 20 74.1% 101 89 16 16 0 18 18 19
10 Netherlands 2 12 36 25.0% 4.1% 60 2 0 0 0 1556 1381 88.7% 2163 50 49 22 34 41 41 0.0 0 9 5 12 70.6% 35 30 3 5 0 7 7 15
11 Poland 2 15 23 39.4% 5.2% 48 0 0 0 1 1059 852 80.4% 1724 55 39 14 67 87 62 0.0 0 8 3 6 66.7% 48 56 3 7 1 7 7 17
12 Portugal 6 22 42 34.3% 9.3% 82 6 0 0 2 1891 1461 77.2% 2958 91 64 41 78 92 86 0.0 2 11 4 10 71.5% 73 90 10 12 0 14 14 16
13 Republic of Ireland 1 7 12 36.8% 5.2% 28 0 0 0 1 851 606 71.2% 1433 43 18 8 45 78 43 1.0 0 23 9 17 65.4% 43 51 11 6 1 10 10 17
14 Russia 5 9 31 22.5% 12.5% 59 2 0 0 1 1602 1345 83.9% 2278 40 40 21 65 74 58 0.0 0 8 3 10 77.0% 34 43 4 6 0 7 7 16
15 Spain 12 42 33 55.9% 16.0% 100 0 1 0 2 4317 3820 88.4% 5585 69 106 44 122 102 79 0.0 5 8 1 15 93.8% 102 83 19 11 0 17 17 18
16 Sweden 5 17 19 47.2% 13.8% 39 3 0 0 1 1192 965 80.9% 1806 44 29 7 56 54 45 0.0 1 12 5 8 61.6% 35 51 7 7 0 9 9 18
17 Ukraine 2 7 26 21.2% 6.0% 38 0 0 0 2 1276 1043 81.7% 1894 33 26 18 65 97 29 0.0 0 4 4 13 76.5% 48 31 4 5 0 9 9 18

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,Year,Population,Total,Violent,Property,Murder,Forcible_Rape,Robbery,Aggravated_assault,Burglary,Larceny_Theft,Vehicle_Theft
0,1960,179323175,3384200,288460,3095700,9110,17190,107840,154320,912100,1855400,328200
1,1961,182992000,3488000,289390,3198600,8740,17220,106670,156760,949600,1913000,336000
2,1962,185771000,3752200,301510,3450700,8530,17550,110860,164570,994300,2089600,366800
3,1963,188483000,4109500,316970,3792500,8640,17650,116470,174210,1086400,2297800,408300
4,1964,191141000,4564600,364220,4200400,9360,21420,130390,203050,1213200,2514400,472800
5,1965,193526000,4739400,387390,4352000,9960,23410,138690,215330,1282500,2572600,496900
6,1966,195576000,5223500,430180,4793300,11040,25820,157990,235330,1410100,2822000,561200
7,1967,197457000,5903400,499930,5403500,12240,27620,202910,257160,1632100,3111600,659800
8,1968,199399000,6720200,595010,6125200,13800,31670,262840,286700,1858900,3482700,783600
9,1969,201385000,7410900,661870,6749000,14760,37170,298850,311090,1981900,3888600,878500
10,1970,203235298,8098000,738820,7359200,16000,37990,349860,334970,2205000,4225800,928400
11,1971,206212000,8588200,816500,7771700,17780,42260,387700,368760,2399300,4424200,948200
12,1972,208230000,8248800,834900,7413900,18670,46850,376290,393090,2375500,4151200,887200
13,1973,209851000,8718100,875910,7842200,19640,51400,384220,420650,2565500,4347900,928800
14,1974,211392000,10253400,974720,9278700,20710,55400,442400,456210,3039200,5262500,977100
15,1975,213124000,11292400,1039710,10252700,20510,56090,470500,492620,3265300,5977700,1009600
16,1976,214659000,11349700,1004210,10345500,18780,57080,427810,500530,3108700,6270800,966000
17,1977,216332000,10984500,1029580,9955000,19120,63500,412610,534350,3071500,5905700,977700
18,1978,218059000,11209000,1085550,10123400,19560,67610,426930,571460,3128300,5991000,1004100
19,1979,220099000,12249500,1208030,11041500,21460,76390,480700,629480,3327700,6601000,1112800
20,1980,225349264,13408300,1344520,12063700,23040,82990,565840,672650,3795200,7136900,1131700
21,1981,229146000,13423800,1361820,12061900,22520,82500,592910,663900,3779700,7194400,1087800
22,1982,231534000,12974400,1322390,11652000,21010,78770,553130,669480,3447100,7142500,1062400
23,1983,233981000,12108600,1258090,10850500,19310,78920,506570,653290,3129900,6712800,1007900
24,1984,236158000,11881800,1273280,10608500,18690,84230,485010,685350,2984400,6591900,1032200
25,1985,238740000,12431400,1328800,11102600,18980,88670,497870,723250,3073300,6926400,1102900
26,1986,240132887,13211869,1489169,11722700,20613,91459,542775,834322,3241410,7257153,1224137
27,1987,242282918,13508700,1483999,12024700,20096,91110,517704,855088,3236184,7499900,1288674
28,1988,245807000,13923100,1566220,12356900,20680,92490,542970,910090,3218100,7705900,1432900
29,1989,248239000,14251400,1646040,12605400,21500,94500,578330,951710,3168200,7872400,1564800
30,1990,248709873,14475600,1820130,12655500,23440,102560,639270,1054860,3073900,7945700,1635900
31,1991,252177000,14872900,1911770,12961100,24700,106590,687730,1092740,3157200,8142200,1661700
32,1992,255082000,14438200,1932270,12505900,23760,109060,672480,1126970,2979900,7915200,1610800
33,1993,257908000,14144800,1926020,12218800,24530,106010,659870,1135610,2834800,7820900,1563100
34,1994,260341000,13989500,1857670,12131900,23330,102220,618950,1113180,2712800,7879800,1539300
35,1995,262755000,13862700,1798790,12063900,21610,97470,580510,1099210,2593800,7997700,1472400
36,1996,265228572,13493863,1688540,11805300,19650,96250,535590,1037050,2506400,7904700,1394200
37,1997,267637000,13194571,1634770,11558175,18208,96153,498534,1023201,2460526,7743760,1354189
38,1998,270296000,12475634,1531044,10944590,16914,93103,446625,974402,2329950,7373886,1240754
39,1999,272690813,11634378,1426044,10208334,15522,89411,409371,911740,2100739,6955520,1152075
40,2000,281421906,11608072,1425486,10182586,15586,90178,408016,911706,2050992,6971590,1160002
41,2001,285317559,11876669,1439480,10437480,16037,90863,423557,909023,2116531,7092267,1228391
42,2002,287973924,11878954,1423677,10455277,16229,95235,420806,891407,2151252,7057370,1246646
43,2003,290690788,11826538,1383676,10442862,16528,93883,414235,859030,2154834,7026802,1261226
44,2004,293656842,11679474,1360088,10319386,16148,95089,401470,847381,2144446,6937089,1237851
45,2005,296507061,11565499,1390745,10174754,16740,94347,417438,862220,2155448,6783447,1235859
46,2006,299398484,11401511,1418043,9983568,17030,92757,447403,860853,2183746,6607013,1192809
47,2007,301621157,11251828,1408337,9843481,16929,90427,445125,855856,2176140,6568572,1095769
48,2008,304374846,11160543,1392628,9767915,16442,90479,443574,842134,2228474,6588046,958629
49,2009,307006550,10762956,1325896,9337060,15399,89241,408742,812514,2203313,6338095,795652
50,2010,309330219,10363873,1251248,9112625,14772,85593,369089,781844,2168457,6204601,739565
51,2011,311587816,10258774,1206031,9052743,14661,84175,354772,752423,2185140,6151095,716508
52,2012,313873685,10219059,1217067,9001992,14866,85141,355051,762009,2109932,6168874,723186
53,2013,316497531,9850445,1199684,8650761,14319,82109,345095,726575,1931835,6018632,700294
54,2014,318857056,9475816,1197987,8277829,14249,84041,325802,741291,1729806,5858496,689527
1 Year Population Total Violent Property Murder Forcible_Rape Robbery Aggravated_assault Burglary Larceny_Theft Vehicle_Theft
2 0 1960 179323175 3384200 288460 3095700 9110 17190 107840 154320 912100 1855400 328200
3 1 1961 182992000 3488000 289390 3198600 8740 17220 106670 156760 949600 1913000 336000
4 2 1962 185771000 3752200 301510 3450700 8530 17550 110860 164570 994300 2089600 366800
5 3 1963 188483000 4109500 316970 3792500 8640 17650 116470 174210 1086400 2297800 408300
6 4 1964 191141000 4564600 364220 4200400 9360 21420 130390 203050 1213200 2514400 472800
7 5 1965 193526000 4739400 387390 4352000 9960 23410 138690 215330 1282500 2572600 496900
8 6 1966 195576000 5223500 430180 4793300 11040 25820 157990 235330 1410100 2822000 561200
9 7 1967 197457000 5903400 499930 5403500 12240 27620 202910 257160 1632100 3111600 659800
10 8 1968 199399000 6720200 595010 6125200 13800 31670 262840 286700 1858900 3482700 783600
11 9 1969 201385000 7410900 661870 6749000 14760 37170 298850 311090 1981900 3888600 878500
12 10 1970 203235298 8098000 738820 7359200 16000 37990 349860 334970 2205000 4225800 928400
13 11 1971 206212000 8588200 816500 7771700 17780 42260 387700 368760 2399300 4424200 948200
14 12 1972 208230000 8248800 834900 7413900 18670 46850 376290 393090 2375500 4151200 887200
15 13 1973 209851000 8718100 875910 7842200 19640 51400 384220 420650 2565500 4347900 928800
16 14 1974 211392000 10253400 974720 9278700 20710 55400 442400 456210 3039200 5262500 977100
17 15 1975 213124000 11292400 1039710 10252700 20510 56090 470500 492620 3265300 5977700 1009600
18 16 1976 214659000 11349700 1004210 10345500 18780 57080 427810 500530 3108700 6270800 966000
19 17 1977 216332000 10984500 1029580 9955000 19120 63500 412610 534350 3071500 5905700 977700
20 18 1978 218059000 11209000 1085550 10123400 19560 67610 426930 571460 3128300 5991000 1004100
21 19 1979 220099000 12249500 1208030 11041500 21460 76390 480700 629480 3327700 6601000 1112800
22 20 1980 225349264 13408300 1344520 12063700 23040 82990 565840 672650 3795200 7136900 1131700
23 21 1981 229146000 13423800 1361820 12061900 22520 82500 592910 663900 3779700 7194400 1087800
24 22 1982 231534000 12974400 1322390 11652000 21010 78770 553130 669480 3447100 7142500 1062400
25 23 1983 233981000 12108600 1258090 10850500 19310 78920 506570 653290 3129900 6712800 1007900
26 24 1984 236158000 11881800 1273280 10608500 18690 84230 485010 685350 2984400 6591900 1032200
27 25 1985 238740000 12431400 1328800 11102600 18980 88670 497870 723250 3073300 6926400 1102900
28 26 1986 240132887 13211869 1489169 11722700 20613 91459 542775 834322 3241410 7257153 1224137
29 27 1987 242282918 13508700 1483999 12024700 20096 91110 517704 855088 3236184 7499900 1288674
30 28 1988 245807000 13923100 1566220 12356900 20680 92490 542970 910090 3218100 7705900 1432900
31 29 1989 248239000 14251400 1646040 12605400 21500 94500 578330 951710 3168200 7872400 1564800
32 30 1990 248709873 14475600 1820130 12655500 23440 102560 639270 1054860 3073900 7945700 1635900
33 31 1991 252177000 14872900 1911770 12961100 24700 106590 687730 1092740 3157200 8142200 1661700
34 32 1992 255082000 14438200 1932270 12505900 23760 109060 672480 1126970 2979900 7915200 1610800
35 33 1993 257908000 14144800 1926020 12218800 24530 106010 659870 1135610 2834800 7820900 1563100
36 34 1994 260341000 13989500 1857670 12131900 23330 102220 618950 1113180 2712800 7879800 1539300
37 35 1995 262755000 13862700 1798790 12063900 21610 97470 580510 1099210 2593800 7997700 1472400
38 36 1996 265228572 13493863 1688540 11805300 19650 96250 535590 1037050 2506400 7904700 1394200
39 37 1997 267637000 13194571 1634770 11558175 18208 96153 498534 1023201 2460526 7743760 1354189
40 38 1998 270296000 12475634 1531044 10944590 16914 93103 446625 974402 2329950 7373886 1240754
41 39 1999 272690813 11634378 1426044 10208334 15522 89411 409371 911740 2100739 6955520 1152075
42 40 2000 281421906 11608072 1425486 10182586 15586 90178 408016 911706 2050992 6971590 1160002
43 41 2001 285317559 11876669 1439480 10437480 16037 90863 423557 909023 2116531 7092267 1228391
44 42 2002 287973924 11878954 1423677 10455277 16229 95235 420806 891407 2151252 7057370 1246646
45 43 2003 290690788 11826538 1383676 10442862 16528 93883 414235 859030 2154834 7026802 1261226
46 44 2004 293656842 11679474 1360088 10319386 16148 95089 401470 847381 2144446 6937089 1237851
47 45 2005 296507061 11565499 1390745 10174754 16740 94347 417438 862220 2155448 6783447 1235859
48 46 2006 299398484 11401511 1418043 9983568 17030 92757 447403 860853 2183746 6607013 1192809
49 47 2007 301621157 11251828 1408337 9843481 16929 90427 445125 855856 2176140 6568572 1095769
50 48 2008 304374846 11160543 1392628 9767915 16442 90479 443574 842134 2228474 6588046 958629
51 49 2009 307006550 10762956 1325896 9337060 15399 89241 408742 812514 2203313 6338095 795652
52 50 2010 309330219 10363873 1251248 9112625 14772 85593 369089 781844 2168457 6204601 739565
53 51 2011 311587816 10258774 1206031 9052743 14661 84175 354772 752423 2185140 6151095 716508
54 52 2012 313873685 10219059 1217067 9001992 14866 85141 355051 762009 2109932 6168874 723186
55 53 2013 316497531 9850445 1199684 8650761 14319 82109 345095 726575 1931835 6018632 700294
56 54 2014 318857056 9475816 1197987 8277829 14249 84041 325802 741291 1729806 5858496 689527

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country,beer_servings,spirit_servings,wine_servings,total_litres_of_pure_alcohol,continent
Afghanistan,0,0,0,0.0,AS
Albania,89,132,54,4.9,EU
Algeria,25,0,14,0.7,AF
Andorra,245,138,312,12.4,EU
Angola,217,57,45,5.9,AF
Antigua & Barbuda,102,128,45,4.9,NA
Argentina,193,25,221,8.3,SA
Armenia,21,179,11,3.8,EU
Australia,261,72,212,10.4,OC
Austria,279,75,191,9.7,EU
Azerbaijan,21,46,5,1.3,EU
Bahamas,122,176,51,6.3,NA
Bahrain,42,63,7,2.0,AS
Bangladesh,0,0,0,0.0,AS
Barbados,143,173,36,6.3,NA
Belarus,142,373,42,14.4,EU
Belgium,295,84,212,10.5,EU
Belize,263,114,8,6.8,NA
Benin,34,4,13,1.1,AF
Bhutan,23,0,0,0.4,AS
Bolivia,167,41,8,3.8,SA
Bosnia-Herzegovina,76,173,8,4.6,EU
Botswana,173,35,35,5.4,AF
Brazil,245,145,16,7.2,SA
Brunei,31,2,1,0.6,AS
Bulgaria,231,252,94,10.3,EU
Burkina Faso,25,7,7,4.3,AF
Burundi,88,0,0,6.3,AF
Cote d'Ivoire,37,1,7,4.0,AF
Cabo Verde,144,56,16,4.0,AF
Cambodia,57,65,1,2.2,AS
Cameroon,147,1,4,5.8,AF
Canada,240,122,100,8.2,NA
Central African Republic,17,2,1,1.8,AF
Chad,15,1,1,0.4,AF
Chile,130,124,172,7.6,SA
China,79,192,8,5.0,AS
Colombia,159,76,3,4.2,SA
Comoros,1,3,1,0.1,AF
Congo,76,1,9,1.7,AF
Cook Islands,0,254,74,5.9,OC
Costa Rica,149,87,11,4.4,NA
Croatia,230,87,254,10.2,EU
Cuba,93,137,5,4.2,NA
Cyprus,192,154,113,8.2,EU
Czech Republic,361,170,134,11.8,EU
North Korea,0,0,0,0.0,AS
DR Congo,32,3,1,2.3,AF
Denmark,224,81,278,10.4,EU
Djibouti,15,44,3,1.1,AF
Dominica,52,286,26,6.6,NA
Dominican Republic,193,147,9,6.2,NA
Ecuador,162,74,3,4.2,SA
Egypt,6,4,1,0.2,AF
El Salvador,52,69,2,2.2,NA
Equatorial Guinea,92,0,233,5.8,AF
Eritrea,18,0,0,0.5,AF
Estonia,224,194,59,9.5,EU
Ethiopia,20,3,0,0.7,AF
Fiji,77,35,1,2.0,OC
Finland,263,133,97,10.0,EU
France,127,151,370,11.8,EU
Gabon,347,98,59,8.9,AF
Gambia,8,0,1,2.4,AF
Georgia,52,100,149,5.4,EU
Germany,346,117,175,11.3,EU
Ghana,31,3,10,1.8,AF
Greece,133,112,218,8.3,EU
Grenada,199,438,28,11.9,NA
Guatemala,53,69,2,2.2,NA
Guinea,9,0,2,0.2,AF
Guinea-Bissau,28,31,21,2.5,AF
Guyana,93,302,1,7.1,SA
Haiti,1,326,1,5.9,NA
Honduras,69,98,2,3.0,NA
Hungary,234,215,185,11.3,EU
Iceland,233,61,78,6.6,EU
India,9,114,0,2.2,AS
Indonesia,5,1,0,0.1,AS
Iran,0,0,0,0.0,AS
Iraq,9,3,0,0.2,AS
Ireland,313,118,165,11.4,EU
Israel,63,69,9,2.5,AS
Italy,85,42,237,6.5,EU
Jamaica,82,97,9,3.4,NA
Japan,77,202,16,7.0,AS
Jordan,6,21,1,0.5,AS
Kazakhstan,124,246,12,6.8,AS
Kenya,58,22,2,1.8,AF
Kiribati,21,34,1,1.0,OC
Kuwait,0,0,0,0.0,AS
Kyrgyzstan,31,97,6,2.4,AS
Laos,62,0,123,6.2,AS
Latvia,281,216,62,10.5,EU
Lebanon,20,55,31,1.9,AS
Lesotho,82,29,0,2.8,AF
Liberia,19,152,2,3.1,AF
Libya,0,0,0,0.0,AF
Lithuania,343,244,56,12.9,EU
Luxembourg,236,133,271,11.4,EU
Madagascar,26,15,4,0.8,AF
Malawi,8,11,1,1.5,AF
Malaysia,13,4,0,0.3,AS
Maldives,0,0,0,0.0,AS
Mali,5,1,1,0.6,AF
Malta,149,100,120,6.6,EU
Marshall Islands,0,0,0,0.0,OC
Mauritania,0,0,0,0.0,AF
Mauritius,98,31,18,2.6,AF
Mexico,238,68,5,5.5,NA
Micronesia,62,50,18,2.3,OC
Monaco,0,0,0,0.0,EU
Mongolia,77,189,8,4.9,AS
Montenegro,31,114,128,4.9,EU
Morocco,12,6,10,0.5,AF
Mozambique,47,18,5,1.3,AF
Myanmar,5,1,0,0.1,AS
Namibia,376,3,1,6.8,AF
Nauru,49,0,8,1.0,OC
Nepal,5,6,0,0.2,AS
Netherlands,251,88,190,9.4,EU
New Zealand,203,79,175,9.3,OC
Nicaragua,78,118,1,3.5,NA
Niger,3,2,1,0.1,AF
Nigeria,42,5,2,9.1,AF
Niue,188,200,7,7.0,OC
Norway,169,71,129,6.7,EU
Oman,22,16,1,0.7,AS
Pakistan,0,0,0,0.0,AS
Palau,306,63,23,6.9,OC
Panama,285,104,18,7.2,NA
Papua New Guinea,44,39,1,1.5,OC
Paraguay,213,117,74,7.3,SA
Peru,163,160,21,6.1,SA
Philippines,71,186,1,4.6,AS
Poland,343,215,56,10.9,EU
Portugal,194,67,339,11.0,EU
Qatar,1,42,7,0.9,AS
South Korea,140,16,9,9.8,AS
Moldova,109,226,18,6.3,EU
Romania,297,122,167,10.4,EU
Russian Federation,247,326,73,11.5,AS
Rwanda,43,2,0,6.8,AF
St. Kitts & Nevis,194,205,32,7.7,NA
St. Lucia,171,315,71,10.1,NA
St. Vincent & the Grenadines,120,221,11,6.3,NA
Samoa,105,18,24,2.6,OC
San Marino,0,0,0,0.0,EU
Sao Tome & Principe,56,38,140,4.2,AF
Saudi Arabia,0,5,0,0.1,AS
Senegal,9,1,7,0.3,AF
Serbia,283,131,127,9.6,EU
Seychelles,157,25,51,4.1,AF
Sierra Leone,25,3,2,6.7,AF
Singapore,60,12,11,1.5,AS
Slovakia,196,293,116,11.4,EU
Slovenia,270,51,276,10.6,EU
Solomon Islands,56,11,1,1.2,OC
Somalia,0,0,0,0.0,AF
South Africa,225,76,81,8.2,AF
Spain,284,157,112,10.0,EU
Sri Lanka,16,104,0,2.2,AS
Sudan,8,13,0,1.7,AF
Suriname,128,178,7,5.6,SA
Swaziland,90,2,2,4.7,AF
Sweden,152,60,186,7.2,EU
Switzerland,185,100,280,10.2,EU
Syria,5,35,16,1.0,AS
Tajikistan,2,15,0,0.3,AS
Thailand,99,258,1,6.4,AS
Macedonia,106,27,86,3.9,EU
Timor-Leste,1,1,4,0.1,AS
Togo,36,2,19,1.3,AF
Tonga,36,21,5,1.1,OC
Trinidad & Tobago,197,156,7,6.4,NA
Tunisia,51,3,20,1.3,AF
Turkey,51,22,7,1.4,AS
Turkmenistan,19,71,32,2.2,AS
Tuvalu,6,41,9,1.0,OC
Uganda,45,9,0,8.3,AF
Ukraine,206,237,45,8.9,EU
United Arab Emirates,16,135,5,2.8,AS
United Kingdom,219,126,195,10.4,EU
Tanzania,36,6,1,5.7,AF
USA,249,158,84,8.7,NA
Uruguay,115,35,220,6.6,SA
Uzbekistan,25,101,8,2.4,AS
Vanuatu,21,18,11,0.9,OC
Venezuela,333,100,3,7.7,SA
Vietnam,111,2,1,2.0,AS
Yemen,6,0,0,0.1,AS
Zambia,32,19,4,2.5,AF
Zimbabwe,64,18,4,4.7,AF
1 country beer_servings spirit_servings wine_servings total_litres_of_pure_alcohol continent
2 Afghanistan 0 0 0 0.0 AS
3 Albania 89 132 54 4.9 EU
4 Algeria 25 0 14 0.7 AF
5 Andorra 245 138 312 12.4 EU
6 Angola 217 57 45 5.9 AF
7 Antigua & Barbuda 102 128 45 4.9 NA
8 Argentina 193 25 221 8.3 SA
9 Armenia 21 179 11 3.8 EU
10 Australia 261 72 212 10.4 OC
11 Austria 279 75 191 9.7 EU
12 Azerbaijan 21 46 5 1.3 EU
13 Bahamas 122 176 51 6.3 NA
14 Bahrain 42 63 7 2.0 AS
15 Bangladesh 0 0 0 0.0 AS
16 Barbados 143 173 36 6.3 NA
17 Belarus 142 373 42 14.4 EU
18 Belgium 295 84 212 10.5 EU
19 Belize 263 114 8 6.8 NA
20 Benin 34 4 13 1.1 AF
21 Bhutan 23 0 0 0.4 AS
22 Bolivia 167 41 8 3.8 SA
23 Bosnia-Herzegovina 76 173 8 4.6 EU
24 Botswana 173 35 35 5.4 AF
25 Brazil 245 145 16 7.2 SA
26 Brunei 31 2 1 0.6 AS
27 Bulgaria 231 252 94 10.3 EU
28 Burkina Faso 25 7 7 4.3 AF
29 Burundi 88 0 0 6.3 AF
30 Cote d'Ivoire 37 1 7 4.0 AF
31 Cabo Verde 144 56 16 4.0 AF
32 Cambodia 57 65 1 2.2 AS
33 Cameroon 147 1 4 5.8 AF
34 Canada 240 122 100 8.2 NA
35 Central African Republic 17 2 1 1.8 AF
36 Chad 15 1 1 0.4 AF
37 Chile 130 124 172 7.6 SA
38 China 79 192 8 5.0 AS
39 Colombia 159 76 3 4.2 SA
40 Comoros 1 3 1 0.1 AF
41 Congo 76 1 9 1.7 AF
42 Cook Islands 0 254 74 5.9 OC
43 Costa Rica 149 87 11 4.4 NA
44 Croatia 230 87 254 10.2 EU
45 Cuba 93 137 5 4.2 NA
46 Cyprus 192 154 113 8.2 EU
47 Czech Republic 361 170 134 11.8 EU
48 North Korea 0 0 0 0.0 AS
49 DR Congo 32 3 1 2.3 AF
50 Denmark 224 81 278 10.4 EU
51 Djibouti 15 44 3 1.1 AF
52 Dominica 52 286 26 6.6 NA
53 Dominican Republic 193 147 9 6.2 NA
54 Ecuador 162 74 3 4.2 SA
55 Egypt 6 4 1 0.2 AF
56 El Salvador 52 69 2 2.2 NA
57 Equatorial Guinea 92 0 233 5.8 AF
58 Eritrea 18 0 0 0.5 AF
59 Estonia 224 194 59 9.5 EU
60 Ethiopia 20 3 0 0.7 AF
61 Fiji 77 35 1 2.0 OC
62 Finland 263 133 97 10.0 EU
63 France 127 151 370 11.8 EU
64 Gabon 347 98 59 8.9 AF
65 Gambia 8 0 1 2.4 AF
66 Georgia 52 100 149 5.4 EU
67 Germany 346 117 175 11.3 EU
68 Ghana 31 3 10 1.8 AF
69 Greece 133 112 218 8.3 EU
70 Grenada 199 438 28 11.9 NA
71 Guatemala 53 69 2 2.2 NA
72 Guinea 9 0 2 0.2 AF
73 Guinea-Bissau 28 31 21 2.5 AF
74 Guyana 93 302 1 7.1 SA
75 Haiti 1 326 1 5.9 NA
76 Honduras 69 98 2 3.0 NA
77 Hungary 234 215 185 11.3 EU
78 Iceland 233 61 78 6.6 EU
79 India 9 114 0 2.2 AS
80 Indonesia 5 1 0 0.1 AS
81 Iran 0 0 0 0.0 AS
82 Iraq 9 3 0 0.2 AS
83 Ireland 313 118 165 11.4 EU
84 Israel 63 69 9 2.5 AS
85 Italy 85 42 237 6.5 EU
86 Jamaica 82 97 9 3.4 NA
87 Japan 77 202 16 7.0 AS
88 Jordan 6 21 1 0.5 AS
89 Kazakhstan 124 246 12 6.8 AS
90 Kenya 58 22 2 1.8 AF
91 Kiribati 21 34 1 1.0 OC
92 Kuwait 0 0 0 0.0 AS
93 Kyrgyzstan 31 97 6 2.4 AS
94 Laos 62 0 123 6.2 AS
95 Latvia 281 216 62 10.5 EU
96 Lebanon 20 55 31 1.9 AS
97 Lesotho 82 29 0 2.8 AF
98 Liberia 19 152 2 3.1 AF
99 Libya 0 0 0 0.0 AF
100 Lithuania 343 244 56 12.9 EU
101 Luxembourg 236 133 271 11.4 EU
102 Madagascar 26 15 4 0.8 AF
103 Malawi 8 11 1 1.5 AF
104 Malaysia 13 4 0 0.3 AS
105 Maldives 0 0 0 0.0 AS
106 Mali 5 1 1 0.6 AF
107 Malta 149 100 120 6.6 EU
108 Marshall Islands 0 0 0 0.0 OC
109 Mauritania 0 0 0 0.0 AF
110 Mauritius 98 31 18 2.6 AF
111 Mexico 238 68 5 5.5 NA
112 Micronesia 62 50 18 2.3 OC
113 Monaco 0 0 0 0.0 EU
114 Mongolia 77 189 8 4.9 AS
115 Montenegro 31 114 128 4.9 EU
116 Morocco 12 6 10 0.5 AF
117 Mozambique 47 18 5 1.3 AF
118 Myanmar 5 1 0 0.1 AS
119 Namibia 376 3 1 6.8 AF
120 Nauru 49 0 8 1.0 OC
121 Nepal 5 6 0 0.2 AS
122 Netherlands 251 88 190 9.4 EU
123 New Zealand 203 79 175 9.3 OC
124 Nicaragua 78 118 1 3.5 NA
125 Niger 3 2 1 0.1 AF
126 Nigeria 42 5 2 9.1 AF
127 Niue 188 200 7 7.0 OC
128 Norway 169 71 129 6.7 EU
129 Oman 22 16 1 0.7 AS
130 Pakistan 0 0 0 0.0 AS
131 Palau 306 63 23 6.9 OC
132 Panama 285 104 18 7.2 NA
133 Papua New Guinea 44 39 1 1.5 OC
134 Paraguay 213 117 74 7.3 SA
135 Peru 163 160 21 6.1 SA
136 Philippines 71 186 1 4.6 AS
137 Poland 343 215 56 10.9 EU
138 Portugal 194 67 339 11.0 EU
139 Qatar 1 42 7 0.9 AS
140 South Korea 140 16 9 9.8 AS
141 Moldova 109 226 18 6.3 EU
142 Romania 297 122 167 10.4 EU
143 Russian Federation 247 326 73 11.5 AS
144 Rwanda 43 2 0 6.8 AF
145 St. Kitts & Nevis 194 205 32 7.7 NA
146 St. Lucia 171 315 71 10.1 NA
147 St. Vincent & the Grenadines 120 221 11 6.3 NA
148 Samoa 105 18 24 2.6 OC
149 San Marino 0 0 0 0.0 EU
150 Sao Tome & Principe 56 38 140 4.2 AF
151 Saudi Arabia 0 5 0 0.1 AS
152 Senegal 9 1 7 0.3 AF
153 Serbia 283 131 127 9.6 EU
154 Seychelles 157 25 51 4.1 AF
155 Sierra Leone 25 3 2 6.7 AF
156 Singapore 60 12 11 1.5 AS
157 Slovakia 196 293 116 11.4 EU
158 Slovenia 270 51 276 10.6 EU
159 Solomon Islands 56 11 1 1.2 OC
160 Somalia 0 0 0 0.0 AF
161 South Africa 225 76 81 8.2 AF
162 Spain 284 157 112 10.0 EU
163 Sri Lanka 16 104 0 2.2 AS
164 Sudan 8 13 0 1.7 AF
165 Suriname 128 178 7 5.6 SA
166 Swaziland 90 2 2 4.7 AF
167 Sweden 152 60 186 7.2 EU
168 Switzerland 185 100 280 10.2 EU
169 Syria 5 35 16 1.0 AS
170 Tajikistan 2 15 0 0.3 AS
171 Thailand 99 258 1 6.4 AS
172 Macedonia 106 27 86 3.9 EU
173 Timor-Leste 1 1 4 0.1 AS
174 Togo 36 2 19 1.3 AF
175 Tonga 36 21 5 1.1 OC
176 Trinidad & Tobago 197 156 7 6.4 NA
177 Tunisia 51 3 20 1.3 AF
178 Turkey 51 22 7 1.4 AS
179 Turkmenistan 19 71 32 2.2 AS
180 Tuvalu 6 41 9 1.0 OC
181 Uganda 45 9 0 8.3 AF
182 Ukraine 206 237 45 8.9 EU
183 United Arab Emirates 16 135 5 2.8 AS
184 United Kingdom 219 126 195 10.4 EU
185 Tanzania 36 6 1 5.7 AF
186 USA 249 158 84 8.7 NA
187 Uruguay 115 35 220 6.6 SA
188 Uzbekistan 25 101 8 2.4 AS
189 Vanuatu 21 18 11 0.9 OC
190 Venezuela 333 100 3 7.7 SA
191 Vietnam 111 2 1 2.0 AS
192 Yemen 6 0 0 0.1 AS
193 Zambia 32 19 4 2.5 AF
194 Zimbabwe 64 18 4 4.7 AF

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5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
5.0,3.6,1.4,0.2,Iris-setosa
5.4,3.9,1.7,0.4,Iris-setosa
4.6,3.4,1.4,0.3,Iris-setosa
5.0,3.4,1.5,0.2,Iris-setosa
4.4,2.9,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.4,3.7,1.5,0.2,Iris-setosa
4.8,3.4,1.6,0.2,Iris-setosa
4.8,3.0,1.4,0.1,Iris-setosa
4.3,3.0,1.1,0.1,Iris-setosa
5.8,4.0,1.2,0.2,Iris-setosa
5.7,4.4,1.5,0.4,Iris-setosa
5.4,3.9,1.3,0.4,Iris-setosa
5.1,3.5,1.4,0.3,Iris-setosa
5.7,3.8,1.7,0.3,Iris-setosa
5.1,3.8,1.5,0.3,Iris-setosa
5.4,3.4,1.7,0.2,Iris-setosa
5.1,3.7,1.5,0.4,Iris-setosa
4.6,3.6,1.0,0.2,Iris-setosa
5.1,3.3,1.7,0.5,Iris-setosa
4.8,3.4,1.9,0.2,Iris-setosa
5.0,3.0,1.6,0.2,Iris-setosa
5.0,3.4,1.6,0.4,Iris-setosa
5.2,3.5,1.5,0.2,Iris-setosa
5.2,3.4,1.4,0.2,Iris-setosa
4.7,3.2,1.6,0.2,Iris-setosa
4.8,3.1,1.6,0.2,Iris-setosa
5.4,3.4,1.5,0.4,Iris-setosa
5.2,4.1,1.5,0.1,Iris-setosa
5.5,4.2,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.0,3.2,1.2,0.2,Iris-setosa
5.5,3.5,1.3,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
4.4,3.0,1.3,0.2,Iris-setosa
5.1,3.4,1.5,0.2,Iris-setosa
5.0,3.5,1.3,0.3,Iris-setosa
4.5,2.3,1.3,0.3,Iris-setosa
4.4,3.2,1.3,0.2,Iris-setosa
5.0,3.5,1.6,0.6,Iris-setosa
5.1,3.8,1.9,0.4,Iris-setosa
4.8,3.0,1.4,0.3,Iris-setosa
5.1,3.8,1.6,0.2,Iris-setosa
4.6,3.2,1.4,0.2,Iris-setosa
5.3,3.7,1.5,0.2,Iris-setosa
5.0,3.3,1.4,0.2,Iris-setosa
7.0,3.2,4.7,1.4,Iris-versicolor
6.4,3.2,4.5,1.5,Iris-versicolor
6.9,3.1,4.9,1.5,Iris-versicolor
5.5,2.3,4.0,1.3,Iris-versicolor
6.5,2.8,4.6,1.5,Iris-versicolor
5.7,2.8,4.5,1.3,Iris-versicolor
6.3,3.3,4.7,1.6,Iris-versicolor
4.9,2.4,3.3,1.0,Iris-versicolor
6.6,2.9,4.6,1.3,Iris-versicolor
5.2,2.7,3.9,1.4,Iris-versicolor
5.0,2.0,3.5,1.0,Iris-versicolor
5.9,3.0,4.2,1.5,Iris-versicolor
6.0,2.2,4.0,1.0,Iris-versicolor
6.1,2.9,4.7,1.4,Iris-versicolor
5.6,2.9,3.6,1.3,Iris-versicolor
6.7,3.1,4.4,1.4,Iris-versicolor
5.6,3.0,4.5,1.5,Iris-versicolor
5.8,2.7,4.1,1.0,Iris-versicolor
6.2,2.2,4.5,1.5,Iris-versicolor
5.6,2.5,3.9,1.1,Iris-versicolor
5.9,3.2,4.8,1.8,Iris-versicolor
6.1,2.8,4.0,1.3,Iris-versicolor
6.3,2.5,4.9,1.5,Iris-versicolor
6.1,2.8,4.7,1.2,Iris-versicolor
6.4,2.9,4.3,1.3,Iris-versicolor
6.6,3.0,4.4,1.4,Iris-versicolor
6.8,2.8,4.8,1.4,Iris-versicolor
6.7,3.0,5.0,1.7,Iris-versicolor
6.0,2.9,4.5,1.5,Iris-versicolor
5.7,2.6,3.5,1.0,Iris-versicolor
5.5,2.4,3.8,1.1,Iris-versicolor
5.5,2.4,3.7,1.0,Iris-versicolor
5.8,2.7,3.9,1.2,Iris-versicolor
6.0,2.7,5.1,1.6,Iris-versicolor
5.4,3.0,4.5,1.5,Iris-versicolor
6.0,3.4,4.5,1.6,Iris-versicolor
6.7,3.1,4.7,1.5,Iris-versicolor
6.3,2.3,4.4,1.3,Iris-versicolor
5.6,3.0,4.1,1.3,Iris-versicolor
5.5,2.5,4.0,1.3,Iris-versicolor
5.5,2.6,4.4,1.2,Iris-versicolor
6.1,3.0,4.6,1.4,Iris-versicolor
5.8,2.6,4.0,1.2,Iris-versicolor
5.0,2.3,3.3,1.0,Iris-versicolor
5.6,2.7,4.2,1.3,Iris-versicolor
5.7,3.0,4.2,1.2,Iris-versicolor
5.7,2.9,4.2,1.3,Iris-versicolor
6.2,2.9,4.3,1.3,Iris-versicolor
5.1,2.5,3.0,1.1,Iris-versicolor
5.7,2.8,4.1,1.3,Iris-versicolor
6.3,3.3,6.0,2.5,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
7.1,3.0,5.9,2.1,Iris-virginica
6.3,2.9,5.6,1.8,Iris-virginica
6.5,3.0,5.8,2.2,Iris-virginica
7.6,3.0,6.6,2.1,Iris-virginica
4.9,2.5,4.5,1.7,Iris-virginica
7.3,2.9,6.3,1.8,Iris-virginica
6.7,2.5,5.8,1.8,Iris-virginica
7.2,3.6,6.1,2.5,Iris-virginica
6.5,3.2,5.1,2.0,Iris-virginica
6.4,2.7,5.3,1.9,Iris-virginica
6.8,3.0,5.5,2.1,Iris-virginica
5.7,2.5,5.0,2.0,Iris-virginica
5.8,2.8,5.1,2.4,Iris-virginica
6.4,3.2,5.3,2.3,Iris-virginica
6.5,3.0,5.5,1.8,Iris-virginica
7.7,3.8,6.7,2.2,Iris-virginica
7.7,2.6,6.9,2.3,Iris-virginica
6.0,2.2,5.0,1.5,Iris-virginica
6.9,3.2,5.7,2.3,Iris-virginica
5.6,2.8,4.9,2.0,Iris-virginica
7.7,2.8,6.7,2.0,Iris-virginica
6.3,2.7,4.9,1.8,Iris-virginica
6.7,3.3,5.7,2.1,Iris-virginica
7.2,3.2,6.0,1.8,Iris-virginica
6.2,2.8,4.8,1.8,Iris-virginica
6.1,3.0,4.9,1.8,Iris-virginica
6.4,2.8,5.6,2.1,Iris-virginica
7.2,3.0,5.8,1.6,Iris-virginica
7.4,2.8,6.1,1.9,Iris-virginica
7.9,3.8,6.4,2.0,Iris-virginica
6.4,2.8,5.6,2.2,Iris-virginica
6.3,2.8,5.1,1.5,Iris-virginica
6.1,2.6,5.6,1.4,Iris-virginica
7.7,3.0,6.1,2.3,Iris-virginica
6.3,3.4,5.6,2.4,Iris-virginica
6.4,3.1,5.5,1.8,Iris-virginica
6.0,3.0,4.8,1.8,Iris-virginica
6.9,3.1,5.4,2.1,Iris-virginica
6.7,3.1,5.6,2.4,Iris-virginica
6.9,3.1,5.1,2.3,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
6.8,3.2,5.9,2.3,Iris-virginica
6.7,3.3,5.7,2.5,Iris-virginica
6.7,3.0,5.2,2.3,Iris-virginica
6.3,2.5,5.0,1.9,Iris-virginica
6.5,3.0,5.2,2.0,Iris-virginica
6.2,3.4,5.4,2.3,Iris-virginica
5.9,3.0,5.1,1.8,Iris-virginica

892
实验 探索Chipotle快餐数据/data/train.csv Normal file
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@ -0,0 +1,892 @@
PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S
51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S
52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S
53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C
54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S
55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C
56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S
57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S
58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C
59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S
60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S
61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C
62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28,
63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S
64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S
65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C
66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C
67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S
68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S
69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S
70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S
71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S
72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S
73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S
74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C
75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S
76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S
77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S
78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S
79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S
80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S
81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S
82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S
83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q
84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S
85,1,2,"Ilett, Miss. Bertha",female,17,0,0,SO/C 14885,10.5,,S
86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,3,0,3101278,15.85,,S
87,0,3,"Ford, Mr. William Neal",male,16,1,3,W./C. 6608,34.375,,S
88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S
89,1,1,"Fortune, Miss. Mabel Helen",female,23,3,2,19950,263,C23 C25 C27,S
90,0,3,"Celotti, Mr. Francesco",male,24,0,0,343275,8.05,,S
91,0,3,"Christmann, Mr. Emil",male,29,0,0,343276,8.05,,S
92,0,3,"Andreasson, Mr. Paul Edvin",male,20,0,0,347466,7.8542,,S
93,0,1,"Chaffee, Mr. Herbert Fuller",male,46,1,0,W.E.P. 5734,61.175,E31,S
94,0,3,"Dean, Mr. Bertram Frank",male,26,1,2,C.A. 2315,20.575,,S
95,0,3,"Coxon, Mr. Daniel",male,59,0,0,364500,7.25,,S
96,0,3,"Shorney, Mr. Charles Joseph",male,,0,0,374910,8.05,,S
97,0,1,"Goldschmidt, Mr. George B",male,71,0,0,PC 17754,34.6542,A5,C
98,1,1,"Greenfield, Mr. William Bertram",male,23,0,1,PC 17759,63.3583,D10 D12,C
99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34,0,1,231919,23,,S
100,0,2,"Kantor, Mr. Sinai",male,34,1,0,244367,26,,S
101,0,3,"Petranec, Miss. Matilda",female,28,0,0,349245,7.8958,,S
102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",male,,0,0,349215,7.8958,,S
103,0,1,"White, Mr. Richard Frasar",male,21,0,1,35281,77.2875,D26,S
104,0,3,"Johansson, Mr. Gustaf Joel",male,33,0,0,7540,8.6542,,S
105,0,3,"Gustafsson, Mr. Anders Vilhelm",male,37,2,0,3101276,7.925,,S
106,0,3,"Mionoff, Mr. Stoytcho",male,28,0,0,349207,7.8958,,S
107,1,3,"Salkjelsvik, Miss. Anna Kristine",female,21,0,0,343120,7.65,,S
108,1,3,"Moss, Mr. Albert Johan",male,,0,0,312991,7.775,,S
109,0,3,"Rekic, Mr. Tido",male,38,0,0,349249,7.8958,,S
110,1,3,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q
111,0,1,"Porter, Mr. Walter Chamberlain",male,47,0,0,110465,52,C110,S
112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C
113,0,3,"Barton, Mr. David John",male,22,0,0,324669,8.05,,S
114,0,3,"Jussila, Miss. Katriina",female,20,1,0,4136,9.825,,S
115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C
116,0,3,"Pekoniemi, Mr. Edvard",male,21,0,0,STON/O 2. 3101294,7.925,,S
117,0,3,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q
118,0,2,"Turpin, Mr. William John Robert",male,29,1,0,11668,21,,S
119,0,1,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C
120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2,4,2,347082,31.275,,S
121,0,2,"Hickman, Mr. Stanley George",male,21,2,0,S.O.C. 14879,73.5,,S
122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S
123,0,2,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C
124,1,2,"Webber, Miss. Susan",female,32.5,0,0,27267,13,E101,S
125,0,1,"White, Mr. Percival Wayland",male,54,0,1,35281,77.2875,D26,S
126,1,3,"Nicola-Yarred, Master. Elias",male,12,1,0,2651,11.2417,,C
127,0,3,"McMahon, Mr. Martin",male,,0,0,370372,7.75,,Q
128,1,3,"Madsen, Mr. Fridtjof Arne",male,24,0,0,C 17369,7.1417,,S
129,1,3,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C
130,0,3,"Ekstrom, Mr. Johan",male,45,0,0,347061,6.975,,S
131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C
132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20,0,0,SOTON/O.Q. 3101307,7.05,,S
133,0,3,"Robins, Mrs. Alexander A (Grace Charity Laury)",female,47,1,0,A/5. 3337,14.5,,S
134,1,2,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,29,1,0,228414,26,,S
135,0,2,"Sobey, Mr. Samuel James Hayden",male,25,0,0,C.A. 29178,13,,S
136,0,2,"Richard, Mr. Emile",male,23,0,0,SC/PARIS 2133,15.0458,,C
137,1,1,"Newsom, Miss. Helen Monypeny",female,19,0,2,11752,26.2833,D47,S
138,0,1,"Futrelle, Mr. Jacques Heath",male,37,1,0,113803,53.1,C123,S
139,0,3,"Osen, Mr. Olaf Elon",male,16,0,0,7534,9.2167,,S
140,0,1,"Giglio, Mr. Victor",male,24,0,0,PC 17593,79.2,B86,C
141,0,3,"Boulos, Mrs. Joseph (Sultana)",female,,0,2,2678,15.2458,,C
142,1,3,"Nysten, Miss. Anna Sofia",female,22,0,0,347081,7.75,,S
143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24,1,0,STON/O2. 3101279,15.85,,S
144,0,3,"Burke, Mr. Jeremiah",male,19,0,0,365222,6.75,,Q
145,0,2,"Andrew, Mr. Edgardo Samuel",male,18,0,0,231945,11.5,,S
146,0,2,"Nicholls, Mr. Joseph Charles",male,19,1,1,C.A. 33112,36.75,,S
147,1,3,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27,0,0,350043,7.7958,,S
148,0,3,"Ford, Miss. Robina Maggie ""Ruby""",female,9,2,2,W./C. 6608,34.375,,S
149,0,2,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,0,2,230080,26,F2,S
150,0,2,"Byles, Rev. Thomas Roussel Davids",male,42,0,0,244310,13,,S
151,0,2,"Bateman, Rev. Robert James",male,51,0,0,S.O.P. 1166,12.525,,S
152,1,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22,1,0,113776,66.6,C2,S
153,0,3,"Meo, Mr. Alfonzo",male,55.5,0,0,A.5. 11206,8.05,,S
154,0,3,"van Billiard, Mr. Austin Blyler",male,40.5,0,2,A/5. 851,14.5,,S
155,0,3,"Olsen, Mr. Ole Martin",male,,0,0,Fa 265302,7.3125,,S
156,0,1,"Williams, Mr. Charles Duane",male,51,0,1,PC 17597,61.3792,,C
157,1,3,"Gilnagh, Miss. Katherine ""Katie""",female,16,0,0,35851,7.7333,,Q
158,0,3,"Corn, Mr. Harry",male,30,0,0,SOTON/OQ 392090,8.05,,S
159,0,3,"Smiljanic, Mr. Mile",male,,0,0,315037,8.6625,,S
160,0,3,"Sage, Master. Thomas Henry",male,,8,2,CA. 2343,69.55,,S
161,0,3,"Cribb, Mr. John Hatfield",male,44,0,1,371362,16.1,,S
162,1,2,"Watt, Mrs. James (Elizabeth ""Bessie"" Inglis Milne)",female,40,0,0,C.A. 33595,15.75,,S
163,0,3,"Bengtsson, Mr. John Viktor",male,26,0,0,347068,7.775,,S
164,0,3,"Calic, Mr. Jovo",male,17,0,0,315093,8.6625,,S
165,0,3,"Panula, Master. Eino Viljami",male,1,4,1,3101295,39.6875,,S
166,1,3,"Goldsmith, Master. Frank John William ""Frankie""",male,9,0,2,363291,20.525,,S
167,1,1,"Chibnall, Mrs. (Edith Martha Bowerman)",female,,0,1,113505,55,E33,S
168,0,3,"Skoog, Mrs. William (Anna Bernhardina Karlsson)",female,45,1,4,347088,27.9,,S
169,0,1,"Baumann, Mr. John D",male,,0,0,PC 17318,25.925,,S
170,0,3,"Ling, Mr. Lee",male,28,0,0,1601,56.4958,,S
171,0,1,"Van der hoef, Mr. Wyckoff",male,61,0,0,111240,33.5,B19,S
172,0,3,"Rice, Master. Arthur",male,4,4,1,382652,29.125,,Q
173,1,3,"Johnson, Miss. Eleanor Ileen",female,1,1,1,347742,11.1333,,S
174,0,3,"Sivola, Mr. Antti Wilhelm",male,21,0,0,STON/O 2. 3101280,7.925,,S
175,0,1,"Smith, Mr. James Clinch",male,56,0,0,17764,30.6958,A7,C
176,0,3,"Klasen, Mr. Klas Albin",male,18,1,1,350404,7.8542,,S
177,0,3,"Lefebre, Master. Henry Forbes",male,,3,1,4133,25.4667,,S
178,0,1,"Isham, Miss. Ann Elizabeth",female,50,0,0,PC 17595,28.7125,C49,C
179,0,2,"Hale, Mr. Reginald",male,30,0,0,250653,13,,S
180,0,3,"Leonard, Mr. Lionel",male,36,0,0,LINE,0,,S
181,0,3,"Sage, Miss. Constance Gladys",female,,8,2,CA. 2343,69.55,,S
182,0,2,"Pernot, Mr. Rene",male,,0,0,SC/PARIS 2131,15.05,,C
183,0,3,"Asplund, Master. Clarence Gustaf Hugo",male,9,4,2,347077,31.3875,,S
184,1,2,"Becker, Master. Richard F",male,1,2,1,230136,39,F4,S
185,1,3,"Kink-Heilmann, Miss. Luise Gretchen",female,4,0,2,315153,22.025,,S
186,0,1,"Rood, Mr. Hugh Roscoe",male,,0,0,113767,50,A32,S
187,1,3,"O'Brien, Mrs. Thomas (Johanna ""Hannah"" Godfrey)",female,,1,0,370365,15.5,,Q
188,1,1,"Romaine, Mr. Charles Hallace (""Mr C Rolmane"")",male,45,0,0,111428,26.55,,S
189,0,3,"Bourke, Mr. John",male,40,1,1,364849,15.5,,Q
190,0,3,"Turcin, Mr. Stjepan",male,36,0,0,349247,7.8958,,S
191,1,2,"Pinsky, Mrs. (Rosa)",female,32,0,0,234604,13,,S
192,0,2,"Carbines, Mr. William",male,19,0,0,28424,13,,S
193,1,3,"Andersen-Jensen, Miss. Carla Christine Nielsine",female,19,1,0,350046,7.8542,,S
194,1,2,"Navratil, Master. Michel M",male,3,1,1,230080,26,F2,S
195,1,1,"Brown, Mrs. James Joseph (Margaret Tobin)",female,44,0,0,PC 17610,27.7208,B4,C
196,1,1,"Lurette, Miss. Elise",female,58,0,0,PC 17569,146.5208,B80,C
197,0,3,"Mernagh, Mr. Robert",male,,0,0,368703,7.75,,Q
198,0,3,"Olsen, Mr. Karl Siegwart Andreas",male,42,0,1,4579,8.4042,,S
199,1,3,"Madigan, Miss. Margaret ""Maggie""",female,,0,0,370370,7.75,,Q
200,0,2,"Yrois, Miss. Henriette (""Mrs Harbeck"")",female,24,0,0,248747,13,,S
201,0,3,"Vande Walle, Mr. Nestor Cyriel",male,28,0,0,345770,9.5,,S
202,0,3,"Sage, Mr. Frederick",male,,8,2,CA. 2343,69.55,,S
203,0,3,"Johanson, Mr. Jakob Alfred",male,34,0,0,3101264,6.4958,,S
204,0,3,"Youseff, Mr. Gerious",male,45.5,0,0,2628,7.225,,C
205,1,3,"Cohen, Mr. Gurshon ""Gus""",male,18,0,0,A/5 3540,8.05,,S
206,0,3,"Strom, Miss. Telma Matilda",female,2,0,1,347054,10.4625,G6,S
207,0,3,"Backstrom, Mr. Karl Alfred",male,32,1,0,3101278,15.85,,S
208,1,3,"Albimona, Mr. Nassef Cassem",male,26,0,0,2699,18.7875,,C
209,1,3,"Carr, Miss. Helen ""Ellen""",female,16,0,0,367231,7.75,,Q
210,1,1,"Blank, Mr. Henry",male,40,0,0,112277,31,A31,C
211,0,3,"Ali, Mr. Ahmed",male,24,0,0,SOTON/O.Q. 3101311,7.05,,S
212,1,2,"Cameron, Miss. Clear Annie",female,35,0,0,F.C.C. 13528,21,,S
213,0,3,"Perkin, Mr. John Henry",male,22,0,0,A/5 21174,7.25,,S
214,0,2,"Givard, Mr. Hans Kristensen",male,30,0,0,250646,13,,S
215,0,3,"Kiernan, Mr. Philip",male,,1,0,367229,7.75,,Q
216,1,1,"Newell, Miss. Madeleine",female,31,1,0,35273,113.275,D36,C
217,1,3,"Honkanen, Miss. Eliina",female,27,0,0,STON/O2. 3101283,7.925,,S
218,0,2,"Jacobsohn, Mr. Sidney Samuel",male,42,1,0,243847,27,,S
219,1,1,"Bazzani, Miss. Albina",female,32,0,0,11813,76.2917,D15,C
220,0,2,"Harris, Mr. Walter",male,30,0,0,W/C 14208,10.5,,S
221,1,3,"Sunderland, Mr. Victor Francis",male,16,0,0,SOTON/OQ 392089,8.05,,S
222,0,2,"Bracken, Mr. James H",male,27,0,0,220367,13,,S
223,0,3,"Green, Mr. George Henry",male,51,0,0,21440,8.05,,S
224,0,3,"Nenkoff, Mr. Christo",male,,0,0,349234,7.8958,,S
225,1,1,"Hoyt, Mr. Frederick Maxfield",male,38,1,0,19943,90,C93,S
226,0,3,"Berglund, Mr. Karl Ivar Sven",male,22,0,0,PP 4348,9.35,,S
227,1,2,"Mellors, Mr. William John",male,19,0,0,SW/PP 751,10.5,,S
228,0,3,"Lovell, Mr. John Hall (""Henry"")",male,20.5,0,0,A/5 21173,7.25,,S
229,0,2,"Fahlstrom, Mr. Arne Jonas",male,18,0,0,236171,13,,S
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231,1,1,"Harris, Mrs. Henry Birkhardt (Irene Wallach)",female,35,1,0,36973,83.475,C83,S
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233,0,2,"Sjostedt, Mr. Ernst Adolf",male,59,0,0,237442,13.5,,S
234,1,3,"Asplund, Miss. Lillian Gertrud",female,5,4,2,347077,31.3875,,S
235,0,2,"Leyson, Mr. Robert William Norman",male,24,0,0,C.A. 29566,10.5,,S
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237,0,2,"Hold, Mr. Stephen",male,44,1,0,26707,26,,S
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240,0,2,"Hunt, Mr. George Henry",male,33,0,0,SCO/W 1585,12.275,,S
241,0,3,"Zabour, Miss. Thamine",female,,1,0,2665,14.4542,,C
242,1,3,"Murphy, Miss. Katherine ""Kate""",female,,1,0,367230,15.5,,Q
243,0,2,"Coleridge, Mr. Reginald Charles",male,29,0,0,W./C. 14263,10.5,,S
244,0,3,"Maenpaa, Mr. Matti Alexanteri",male,22,0,0,STON/O 2. 3101275,7.125,,S
245,0,3,"Attalah, Mr. Sleiman",male,30,0,0,2694,7.225,,C
246,0,1,"Minahan, Dr. William Edward",male,44,2,0,19928,90,C78,Q
247,0,3,"Lindahl, Miss. Agda Thorilda Viktoria",female,25,0,0,347071,7.775,,S
248,1,2,"Hamalainen, Mrs. William (Anna)",female,24,0,2,250649,14.5,,S
249,1,1,"Beckwith, Mr. Richard Leonard",male,37,1,1,11751,52.5542,D35,S
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252,0,3,"Strom, Mrs. Wilhelm (Elna Matilda Persson)",female,29,1,1,347054,10.4625,G6,S
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255,0,3,"Rosblom, Mrs. Viktor (Helena Wilhelmina)",female,41,0,2,370129,20.2125,,S
256,1,3,"Touma, Mrs. Darwis (Hanne Youssef Razi)",female,29,0,2,2650,15.2458,,C
257,1,1,"Thorne, Mrs. Gertrude Maybelle",female,,0,0,PC 17585,79.2,,C
258,1,1,"Cherry, Miss. Gladys",female,30,0,0,110152,86.5,B77,S
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260,1,2,"Parrish, Mrs. (Lutie Davis)",female,50,0,1,230433,26,,S
261,0,3,"Smith, Mr. Thomas",male,,0,0,384461,7.75,,Q
262,1,3,"Asplund, Master. Edvin Rojj Felix",male,3,4,2,347077,31.3875,,S
263,0,1,"Taussig, Mr. Emil",male,52,1,1,110413,79.65,E67,S
264,0,1,"Harrison, Mr. William",male,40,0,0,112059,0,B94,S
265,0,3,"Henry, Miss. Delia",female,,0,0,382649,7.75,,Q
266,0,2,"Reeves, Mr. David",male,36,0,0,C.A. 17248,10.5,,S
267,0,3,"Panula, Mr. Ernesti Arvid",male,16,4,1,3101295,39.6875,,S
268,1,3,"Persson, Mr. Ernst Ulrik",male,25,1,0,347083,7.775,,S
269,1,1,"Graham, Mrs. William Thompson (Edith Junkins)",female,58,0,1,PC 17582,153.4625,C125,S
270,1,1,"Bissette, Miss. Amelia",female,35,0,0,PC 17760,135.6333,C99,S
271,0,1,"Cairns, Mr. Alexander",male,,0,0,113798,31,,S
272,1,3,"Tornquist, Mr. William Henry",male,25,0,0,LINE,0,,S
273,1,2,"Mellinger, Mrs. (Elizabeth Anne Maidment)",female,41,0,1,250644,19.5,,S
274,0,1,"Natsch, Mr. Charles H",male,37,0,1,PC 17596,29.7,C118,C
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276,1,1,"Andrews, Miss. Kornelia Theodosia",female,63,1,0,13502,77.9583,D7,S
277,0,3,"Lindblom, Miss. Augusta Charlotta",female,45,0,0,347073,7.75,,S
278,0,2,"Parkes, Mr. Francis ""Frank""",male,,0,0,239853,0,,S
279,0,3,"Rice, Master. Eric",male,7,4,1,382652,29.125,,Q
280,1,3,"Abbott, Mrs. Stanton (Rosa Hunt)",female,35,1,1,C.A. 2673,20.25,,S
281,0,3,"Duane, Mr. Frank",male,65,0,0,336439,7.75,,Q
282,0,3,"Olsson, Mr. Nils Johan Goransson",male,28,0,0,347464,7.8542,,S
283,0,3,"de Pelsmaeker, Mr. Alfons",male,16,0,0,345778,9.5,,S
284,1,3,"Dorking, Mr. Edward Arthur",male,19,0,0,A/5. 10482,8.05,,S
285,0,1,"Smith, Mr. Richard William",male,,0,0,113056,26,A19,S
286,0,3,"Stankovic, Mr. Ivan",male,33,0,0,349239,8.6625,,C
287,1,3,"de Mulder, Mr. Theodore",male,30,0,0,345774,9.5,,S
288,0,3,"Naidenoff, Mr. Penko",male,22,0,0,349206,7.8958,,S
289,1,2,"Hosono, Mr. Masabumi",male,42,0,0,237798,13,,S
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291,1,1,"Barber, Miss. Ellen ""Nellie""",female,26,0,0,19877,78.85,,S
292,1,1,"Bishop, Mrs. Dickinson H (Helen Walton)",female,19,1,0,11967,91.0792,B49,C
293,0,2,"Levy, Mr. Rene Jacques",male,36,0,0,SC/Paris 2163,12.875,D,C
294,0,3,"Haas, Miss. Aloisia",female,24,0,0,349236,8.85,,S
295,0,3,"Mineff, Mr. Ivan",male,24,0,0,349233,7.8958,,S
296,0,1,"Lewy, Mr. Ervin G",male,,0,0,PC 17612,27.7208,,C
297,0,3,"Hanna, Mr. Mansour",male,23.5,0,0,2693,7.2292,,C
298,0,1,"Allison, Miss. Helen Loraine",female,2,1,2,113781,151.55,C22 C26,S
299,1,1,"Saalfeld, Mr. Adolphe",male,,0,0,19988,30.5,C106,S
300,1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)",female,50,0,1,PC 17558,247.5208,B58 B60,C
301,1,3,"Kelly, Miss. Anna Katherine ""Annie Kate""",female,,0,0,9234,7.75,,Q
302,1,3,"McCoy, Mr. Bernard",male,,2,0,367226,23.25,,Q
303,0,3,"Johnson, Mr. William Cahoone Jr",male,19,0,0,LINE,0,,S
304,1,2,"Keane, Miss. Nora A",female,,0,0,226593,12.35,E101,Q
305,0,3,"Williams, Mr. Howard Hugh ""Harry""",male,,0,0,A/5 2466,8.05,,S
306,1,1,"Allison, Master. Hudson Trevor",male,0.92,1,2,113781,151.55,C22 C26,S
307,1,1,"Fleming, Miss. Margaret",female,,0,0,17421,110.8833,,C
308,1,1,"Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)",female,17,1,0,PC 17758,108.9,C65,C
309,0,2,"Abelson, Mr. Samuel",male,30,1,0,P/PP 3381,24,,C
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311,1,1,"Hays, Miss. Margaret Bechstein",female,24,0,0,11767,83.1583,C54,C
312,1,1,"Ryerson, Miss. Emily Borie",female,18,2,2,PC 17608,262.375,B57 B59 B63 B66,C
313,0,2,"Lahtinen, Mrs. William (Anna Sylfven)",female,26,1,1,250651,26,,S
314,0,3,"Hendekovic, Mr. Ignjac",male,28,0,0,349243,7.8958,,S
315,0,2,"Hart, Mr. Benjamin",male,43,1,1,F.C.C. 13529,26.25,,S
316,1,3,"Nilsson, Miss. Helmina Josefina",female,26,0,0,347470,7.8542,,S
317,1,2,"Kantor, Mrs. Sinai (Miriam Sternin)",female,24,1,0,244367,26,,S
318,0,2,"Moraweck, Dr. Ernest",male,54,0,0,29011,14,,S
319,1,1,"Wick, Miss. Mary Natalie",female,31,0,2,36928,164.8667,C7,S
320,1,1,"Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)",female,40,1,1,16966,134.5,E34,C
321,0,3,"Dennis, Mr. Samuel",male,22,0,0,A/5 21172,7.25,,S
322,0,3,"Danoff, Mr. Yoto",male,27,0,0,349219,7.8958,,S
323,1,2,"Slayter, Miss. Hilda Mary",female,30,0,0,234818,12.35,,Q
324,1,2,"Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)",female,22,1,1,248738,29,,S
325,0,3,"Sage, Mr. George John Jr",male,,8,2,CA. 2343,69.55,,S
326,1,1,"Young, Miss. Marie Grice",female,36,0,0,PC 17760,135.6333,C32,C
327,0,3,"Nysveen, Mr. Johan Hansen",male,61,0,0,345364,6.2375,,S
328,1,2,"Ball, Mrs. (Ada E Hall)",female,36,0,0,28551,13,D,S
329,1,3,"Goldsmith, Mrs. Frank John (Emily Alice Brown)",female,31,1,1,363291,20.525,,S
330,1,1,"Hippach, Miss. Jean Gertrude",female,16,0,1,111361,57.9792,B18,C
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332,0,1,"Partner, Mr. Austen",male,45.5,0,0,113043,28.5,C124,S
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335,1,1,"Frauenthal, Mrs. Henry William (Clara Heinsheimer)",female,,1,0,PC 17611,133.65,,S
336,0,3,"Denkoff, Mr. Mitto",male,,0,0,349225,7.8958,,S
337,0,1,"Pears, Mr. Thomas Clinton",male,29,1,0,113776,66.6,C2,S
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339,1,3,"Dahl, Mr. Karl Edwart",male,45,0,0,7598,8.05,,S
340,0,1,"Blackwell, Mr. Stephen Weart",male,45,0,0,113784,35.5,T,S
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343,0,2,"Collander, Mr. Erik Gustaf",male,28,0,0,248740,13,,S
344,0,2,"Sedgwick, Mr. Charles Frederick Waddington",male,25,0,0,244361,13,,S
345,0,2,"Fox, Mr. Stanley Hubert",male,36,0,0,229236,13,,S
346,1,2,"Brown, Miss. Amelia ""Mildred""",female,24,0,0,248733,13,F33,S
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348,1,3,"Davison, Mrs. Thomas Henry (Mary E Finck)",female,,1,0,386525,16.1,,S
349,1,3,"Coutts, Master. William Loch ""William""",male,3,1,1,C.A. 37671,15.9,,S
350,0,3,"Dimic, Mr. Jovan",male,42,0,0,315088,8.6625,,S
351,0,3,"Odahl, Mr. Nils Martin",male,23,0,0,7267,9.225,,S
352,0,1,"Williams-Lambert, Mr. Fletcher Fellows",male,,0,0,113510,35,C128,S
353,0,3,"Elias, Mr. Tannous",male,15,1,1,2695,7.2292,,C
354,0,3,"Arnold-Franchi, Mr. Josef",male,25,1,0,349237,17.8,,S
355,0,3,"Yousif, Mr. Wazli",male,,0,0,2647,7.225,,C
356,0,3,"Vanden Steen, Mr. Leo Peter",male,28,0,0,345783,9.5,,S
357,1,1,"Bowerman, Miss. Elsie Edith",female,22,0,1,113505,55,E33,S
358,0,2,"Funk, Miss. Annie Clemmer",female,38,0,0,237671,13,,S
359,1,3,"McGovern, Miss. Mary",female,,0,0,330931,7.8792,,Q
360,1,3,"Mockler, Miss. Helen Mary ""Ellie""",female,,0,0,330980,7.8792,,Q
361,0,3,"Skoog, Mr. Wilhelm",male,40,1,4,347088,27.9,,S
362,0,2,"del Carlo, Mr. Sebastiano",male,29,1,0,SC/PARIS 2167,27.7208,,C
363,0,3,"Barbara, Mrs. (Catherine David)",female,45,0,1,2691,14.4542,,C
364,0,3,"Asim, Mr. Adola",male,35,0,0,SOTON/O.Q. 3101310,7.05,,S
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366,0,3,"Adahl, Mr. Mauritz Nils Martin",male,30,0,0,C 7076,7.25,,S
367,1,1,"Warren, Mrs. Frank Manley (Anna Sophia Atkinson)",female,60,1,0,110813,75.25,D37,C
368,1,3,"Moussa, Mrs. (Mantoura Boulos)",female,,0,0,2626,7.2292,,C
369,1,3,"Jermyn, Miss. Annie",female,,0,0,14313,7.75,,Q
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373,0,3,"Beavan, Mr. William Thomas",male,19,0,0,323951,8.05,,S
374,0,1,"Ringhini, Mr. Sante",male,22,0,0,PC 17760,135.6333,,C
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376,1,1,"Meyer, Mrs. Edgar Joseph (Leila Saks)",female,,1,0,PC 17604,82.1708,,C
377,1,3,"Landergren, Miss. Aurora Adelia",female,22,0,0,C 7077,7.25,,S
378,0,1,"Widener, Mr. Harry Elkins",male,27,0,2,113503,211.5,C82,C
379,0,3,"Betros, Mr. Tannous",male,20,0,0,2648,4.0125,,C
380,0,3,"Gustafsson, Mr. Karl Gideon",male,19,0,0,347069,7.775,,S
381,1,1,"Bidois, Miss. Rosalie",female,42,0,0,PC 17757,227.525,,C
382,1,3,"Nakid, Miss. Maria (""Mary"")",female,1,0,2,2653,15.7417,,C
383,0,3,"Tikkanen, Mr. Juho",male,32,0,0,STON/O 2. 3101293,7.925,,S
384,1,1,"Holverson, Mrs. Alexander Oskar (Mary Aline Towner)",female,35,1,0,113789,52,,S
385,0,3,"Plotcharsky, Mr. Vasil",male,,0,0,349227,7.8958,,S
386,0,2,"Davies, Mr. Charles Henry",male,18,0,0,S.O.C. 14879,73.5,,S
387,0,3,"Goodwin, Master. Sidney Leonard",male,1,5,2,CA 2144,46.9,,S
388,1,2,"Buss, Miss. Kate",female,36,0,0,27849,13,,S
389,0,3,"Sadlier, Mr. Matthew",male,,0,0,367655,7.7292,,Q
390,1,2,"Lehmann, Miss. Bertha",female,17,0,0,SC 1748,12,,C
391,1,1,"Carter, Mr. William Ernest",male,36,1,2,113760,120,B96 B98,S
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393,0,3,"Gustafsson, Mr. Johan Birger",male,28,2,0,3101277,7.925,,S
394,1,1,"Newell, Miss. Marjorie",female,23,1,0,35273,113.275,D36,C
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397,0,3,"Olsson, Miss. Elina",female,31,0,0,350407,7.8542,,S
398,0,2,"McKane, Mr. Peter David",male,46,0,0,28403,26,,S
399,0,2,"Pain, Dr. Alfred",male,23,0,0,244278,10.5,,S
400,1,2,"Trout, Mrs. William H (Jessie L)",female,28,0,0,240929,12.65,,S
401,1,3,"Niskanen, Mr. Juha",male,39,0,0,STON/O 2. 3101289,7.925,,S
402,0,3,"Adams, Mr. John",male,26,0,0,341826,8.05,,S
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404,0,3,"Hakkarainen, Mr. Pekka Pietari",male,28,1,0,STON/O2. 3101279,15.85,,S
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406,0,2,"Gale, Mr. Shadrach",male,34,1,0,28664,21,,S
407,0,3,"Widegren, Mr. Carl/Charles Peter",male,51,0,0,347064,7.75,,S
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409,0,3,"Birkeland, Mr. Hans Martin Monsen",male,21,0,0,312992,7.775,,S
410,0,3,"Lefebre, Miss. Ida",female,,3,1,4133,25.4667,,S
411,0,3,"Sdycoff, Mr. Todor",male,,0,0,349222,7.8958,,S
412,0,3,"Hart, Mr. Henry",male,,0,0,394140,6.8583,,Q
413,1,1,"Minahan, Miss. Daisy E",female,33,1,0,19928,90,C78,Q
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415,1,3,"Sundman, Mr. Johan Julian",male,44,0,0,STON/O 2. 3101269,7.925,,S
416,0,3,"Meek, Mrs. Thomas (Annie Louise Rowley)",female,,0,0,343095,8.05,,S
417,1,2,"Drew, Mrs. James Vivian (Lulu Thorne Christian)",female,34,1,1,28220,32.5,,S
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419,0,2,"Matthews, Mr. William John",male,30,0,0,28228,13,,S
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421,0,3,"Gheorgheff, Mr. Stanio",male,,0,0,349254,7.8958,,C
422,0,3,"Charters, Mr. David",male,21,0,0,A/5. 13032,7.7333,,Q
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424,0,3,"Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)",female,28,1,1,347080,14.4,,S
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426,0,3,"Wiseman, Mr. Phillippe",male,,0,0,A/4. 34244,7.25,,S
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428,1,2,"Phillips, Miss. Kate Florence (""Mrs Kate Louise Phillips Marshall"")",female,19,0,0,250655,26,,S
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435,0,1,"Silvey, Mr. William Baird",male,50,1,0,13507,55.9,E44,S
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438,1,2,"Richards, Mrs. Sidney (Emily Hocking)",female,24,2,3,29106,18.75,,S
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441,1,2,"Hart, Mrs. Benjamin (Esther Ada Bloomfield)",female,45,1,1,F.C.C. 13529,26.25,,S
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443,0,3,"Petterson, Mr. Johan Emil",male,25,1,0,347076,7.775,,S
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452,0,3,"Hagland, Mr. Ingvald Olai Olsen",male,,1,0,65303,19.9667,,S
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529,0,3,"Salonen, Mr. Johan Werner",male,39,0,0,3101296,7.925,,S
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544,1,2,"Beane, Mr. Edward",male,32,1,0,2908,26,,S
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563,0,2,"Norman, Mr. Robert Douglas",male,28,0,0,218629,13.5,,S
564,0,3,"Simmons, Mr. John",male,,0,0,SOTON/OQ 392082,8.05,,S
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674,1,2,"Wilhelms, Mr. Charles",male,31,0,0,244270,13,,S
675,0,2,"Watson, Mr. Ennis Hastings",male,,0,0,239856,0,,S
676,0,3,"Edvardsson, Mr. Gustaf Hjalmar",male,18,0,0,349912,7.775,,S
677,0,3,"Sawyer, Mr. Frederick Charles",male,24.5,0,0,342826,8.05,,S
678,1,3,"Turja, Miss. Anna Sofia",female,18,0,0,4138,9.8417,,S
679,0,3,"Goodwin, Mrs. Frederick (Augusta Tyler)",female,43,1,6,CA 2144,46.9,,S
680,1,1,"Cardeza, Mr. Thomas Drake Martinez",male,36,0,1,PC 17755,512.3292,B51 B53 B55,C
681,0,3,"Peters, Miss. Katie",female,,0,0,330935,8.1375,,Q
682,1,1,"Hassab, Mr. Hammad",male,27,0,0,PC 17572,76.7292,D49,C
683,0,3,"Olsvigen, Mr. Thor Anderson",male,20,0,0,6563,9.225,,S
684,0,3,"Goodwin, Mr. Charles Edward",male,14,5,2,CA 2144,46.9,,S
685,0,2,"Brown, Mr. Thomas William Solomon",male,60,1,1,29750,39,,S
686,0,2,"Laroche, Mr. Joseph Philippe Lemercier",male,25,1,2,SC/Paris 2123,41.5792,,C
687,0,3,"Panula, Mr. Jaako Arnold",male,14,4,1,3101295,39.6875,,S
688,0,3,"Dakic, Mr. Branko",male,19,0,0,349228,10.1708,,S
689,0,3,"Fischer, Mr. Eberhard Thelander",male,18,0,0,350036,7.7958,,S
690,1,1,"Madill, Miss. Georgette Alexandra",female,15,0,1,24160,211.3375,B5,S
691,1,1,"Dick, Mr. Albert Adrian",male,31,1,0,17474,57,B20,S
692,1,3,"Karun, Miss. Manca",female,4,0,1,349256,13.4167,,C
693,1,3,"Lam, Mr. Ali",male,,0,0,1601,56.4958,,S
694,0,3,"Saad, Mr. Khalil",male,25,0,0,2672,7.225,,C
695,0,1,"Weir, Col. John",male,60,0,0,113800,26.55,,S
696,0,2,"Chapman, Mr. Charles Henry",male,52,0,0,248731,13.5,,S
697,0,3,"Kelly, Mr. James",male,44,0,0,363592,8.05,,S
698,1,3,"Mullens, Miss. Katherine ""Katie""",female,,0,0,35852,7.7333,,Q
699,0,1,"Thayer, Mr. John Borland",male,49,1,1,17421,110.8833,C68,C
700,0,3,"Humblen, Mr. Adolf Mathias Nicolai Olsen",male,42,0,0,348121,7.65,F G63,S
701,1,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)",female,18,1,0,PC 17757,227.525,C62 C64,C
702,1,1,"Silverthorne, Mr. Spencer Victor",male,35,0,0,PC 17475,26.2875,E24,S
703,0,3,"Barbara, Miss. Saiide",female,18,0,1,2691,14.4542,,C
704,0,3,"Gallagher, Mr. Martin",male,25,0,0,36864,7.7417,,Q
705,0,3,"Hansen, Mr. Henrik Juul",male,26,1,0,350025,7.8542,,S
706,0,2,"Morley, Mr. Henry Samuel (""Mr Henry Marshall"")",male,39,0,0,250655,26,,S
707,1,2,"Kelly, Mrs. Florence ""Fannie""",female,45,0,0,223596,13.5,,S
708,1,1,"Calderhead, Mr. Edward Pennington",male,42,0,0,PC 17476,26.2875,E24,S
709,1,1,"Cleaver, Miss. Alice",female,22,0,0,113781,151.55,,S
710,1,3,"Moubarek, Master. Halim Gonios (""William George"")",male,,1,1,2661,15.2458,,C
711,1,1,"Mayne, Mlle. Berthe Antonine (""Mrs de Villiers"")",female,24,0,0,PC 17482,49.5042,C90,C
712,0,1,"Klaber, Mr. Herman",male,,0,0,113028,26.55,C124,S
713,1,1,"Taylor, Mr. Elmer Zebley",male,48,1,0,19996,52,C126,S
714,0,3,"Larsson, Mr. August Viktor",male,29,0,0,7545,9.4833,,S
715,0,2,"Greenberg, Mr. Samuel",male,52,0,0,250647,13,,S
716,0,3,"Soholt, Mr. Peter Andreas Lauritz Andersen",male,19,0,0,348124,7.65,F G73,S
717,1,1,"Endres, Miss. Caroline Louise",female,38,0,0,PC 17757,227.525,C45,C
718,1,2,"Troutt, Miss. Edwina Celia ""Winnie""",female,27,0,0,34218,10.5,E101,S
719,0,3,"McEvoy, Mr. Michael",male,,0,0,36568,15.5,,Q
720,0,3,"Johnson, Mr. Malkolm Joackim",male,33,0,0,347062,7.775,,S
721,1,2,"Harper, Miss. Annie Jessie ""Nina""",female,6,0,1,248727,33,,S
722,0,3,"Jensen, Mr. Svend Lauritz",male,17,1,0,350048,7.0542,,S
723,0,2,"Gillespie, Mr. William Henry",male,34,0,0,12233,13,,S
724,0,2,"Hodges, Mr. Henry Price",male,50,0,0,250643,13,,S
725,1,1,"Chambers, Mr. Norman Campbell",male,27,1,0,113806,53.1,E8,S
726,0,3,"Oreskovic, Mr. Luka",male,20,0,0,315094,8.6625,,S
727,1,2,"Renouf, Mrs. Peter Henry (Lillian Jefferys)",female,30,3,0,31027,21,,S
728,1,3,"Mannion, Miss. Margareth",female,,0,0,36866,7.7375,,Q
729,0,2,"Bryhl, Mr. Kurt Arnold Gottfrid",male,25,1,0,236853,26,,S
730,0,3,"Ilmakangas, Miss. Pieta Sofia",female,25,1,0,STON/O2. 3101271,7.925,,S
731,1,1,"Allen, Miss. Elisabeth Walton",female,29,0,0,24160,211.3375,B5,S
732,0,3,"Hassan, Mr. Houssein G N",male,11,0,0,2699,18.7875,,C
733,0,2,"Knight, Mr. Robert J",male,,0,0,239855,0,,S
734,0,2,"Berriman, Mr. William John",male,23,0,0,28425,13,,S
735,0,2,"Troupiansky, Mr. Moses Aaron",male,23,0,0,233639,13,,S
736,0,3,"Williams, Mr. Leslie",male,28.5,0,0,54636,16.1,,S
737,0,3,"Ford, Mrs. Edward (Margaret Ann Watson)",female,48,1,3,W./C. 6608,34.375,,S
738,1,1,"Lesurer, Mr. Gustave J",male,35,0,0,PC 17755,512.3292,B101,C
739,0,3,"Ivanoff, Mr. Kanio",male,,0,0,349201,7.8958,,S
740,0,3,"Nankoff, Mr. Minko",male,,0,0,349218,7.8958,,S
741,1,1,"Hawksford, Mr. Walter James",male,,0,0,16988,30,D45,S
742,0,1,"Cavendish, Mr. Tyrell William",male,36,1,0,19877,78.85,C46,S
743,1,1,"Ryerson, Miss. Susan Parker ""Suzette""",female,21,2,2,PC 17608,262.375,B57 B59 B63 B66,C
744,0,3,"McNamee, Mr. Neal",male,24,1,0,376566,16.1,,S
745,1,3,"Stranden, Mr. Juho",male,31,0,0,STON/O 2. 3101288,7.925,,S
746,0,1,"Crosby, Capt. Edward Gifford",male,70,1,1,WE/P 5735,71,B22,S
747,0,3,"Abbott, Mr. Rossmore Edward",male,16,1,1,C.A. 2673,20.25,,S
748,1,2,"Sinkkonen, Miss. Anna",female,30,0,0,250648,13,,S
749,0,1,"Marvin, Mr. Daniel Warner",male,19,1,0,113773,53.1,D30,S
750,0,3,"Connaghton, Mr. Michael",male,31,0,0,335097,7.75,,Q
751,1,2,"Wells, Miss. Joan",female,4,1,1,29103,23,,S
752,1,3,"Moor, Master. Meier",male,6,0,1,392096,12.475,E121,S
753,0,3,"Vande Velde, Mr. Johannes Joseph",male,33,0,0,345780,9.5,,S
754,0,3,"Jonkoff, Mr. Lalio",male,23,0,0,349204,7.8958,,S
755,1,2,"Herman, Mrs. Samuel (Jane Laver)",female,48,1,2,220845,65,,S
756,1,2,"Hamalainen, Master. Viljo",male,0.67,1,1,250649,14.5,,S
757,0,3,"Carlsson, Mr. August Sigfrid",male,28,0,0,350042,7.7958,,S
758,0,2,"Bailey, Mr. Percy Andrew",male,18,0,0,29108,11.5,,S
759,0,3,"Theobald, Mr. Thomas Leonard",male,34,0,0,363294,8.05,,S
760,1,1,"Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)",female,33,0,0,110152,86.5,B77,S
761,0,3,"Garfirth, Mr. John",male,,0,0,358585,14.5,,S
762,0,3,"Nirva, Mr. Iisakki Antino Aijo",male,41,0,0,SOTON/O2 3101272,7.125,,S
763,1,3,"Barah, Mr. Hanna Assi",male,20,0,0,2663,7.2292,,C
764,1,1,"Carter, Mrs. William Ernest (Lucile Polk)",female,36,1,2,113760,120,B96 B98,S
765,0,3,"Eklund, Mr. Hans Linus",male,16,0,0,347074,7.775,,S
766,1,1,"Hogeboom, Mrs. John C (Anna Andrews)",female,51,1,0,13502,77.9583,D11,S
767,0,1,"Brewe, Dr. Arthur Jackson",male,,0,0,112379,39.6,,C
768,0,3,"Mangan, Miss. Mary",female,30.5,0,0,364850,7.75,,Q
769,0,3,"Moran, Mr. Daniel J",male,,1,0,371110,24.15,,Q
770,0,3,"Gronnestad, Mr. Daniel Danielsen",male,32,0,0,8471,8.3625,,S
771,0,3,"Lievens, Mr. Rene Aime",male,24,0,0,345781,9.5,,S
772,0,3,"Jensen, Mr. Niels Peder",male,48,0,0,350047,7.8542,,S
773,0,2,"Mack, Mrs. (Mary)",female,57,0,0,S.O./P.P. 3,10.5,E77,S
774,0,3,"Elias, Mr. Dibo",male,,0,0,2674,7.225,,C
775,1,2,"Hocking, Mrs. Elizabeth (Eliza Needs)",female,54,1,3,29105,23,,S
776,0,3,"Myhrman, Mr. Pehr Fabian Oliver Malkolm",male,18,0,0,347078,7.75,,S
777,0,3,"Tobin, Mr. Roger",male,,0,0,383121,7.75,F38,Q
778,1,3,"Emanuel, Miss. Virginia Ethel",female,5,0,0,364516,12.475,,S
779,0,3,"Kilgannon, Mr. Thomas J",male,,0,0,36865,7.7375,,Q
780,1,1,"Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)",female,43,0,1,24160,211.3375,B3,S
781,1,3,"Ayoub, Miss. Banoura",female,13,0,0,2687,7.2292,,C
782,1,1,"Dick, Mrs. Albert Adrian (Vera Gillespie)",female,17,1,0,17474,57,B20,S
783,0,1,"Long, Mr. Milton Clyde",male,29,0,0,113501,30,D6,S
784,0,3,"Johnston, Mr. Andrew G",male,,1,2,W./C. 6607,23.45,,S
785,0,3,"Ali, Mr. William",male,25,0,0,SOTON/O.Q. 3101312,7.05,,S
786,0,3,"Harmer, Mr. Abraham (David Lishin)",male,25,0,0,374887,7.25,,S
787,1,3,"Sjoblom, Miss. Anna Sofia",female,18,0,0,3101265,7.4958,,S
788,0,3,"Rice, Master. George Hugh",male,8,4,1,382652,29.125,,Q
789,1,3,"Dean, Master. Bertram Vere",male,1,1,2,C.A. 2315,20.575,,S
790,0,1,"Guggenheim, Mr. Benjamin",male,46,0,0,PC 17593,79.2,B82 B84,C
791,0,3,"Keane, Mr. Andrew ""Andy""",male,,0,0,12460,7.75,,Q
792,0,2,"Gaskell, Mr. Alfred",male,16,0,0,239865,26,,S
793,0,3,"Sage, Miss. Stella Anna",female,,8,2,CA. 2343,69.55,,S
794,0,1,"Hoyt, Mr. William Fisher",male,,0,0,PC 17600,30.6958,,C
795,0,3,"Dantcheff, Mr. Ristiu",male,25,0,0,349203,7.8958,,S
796,0,2,"Otter, Mr. Richard",male,39,0,0,28213,13,,S
797,1,1,"Leader, Dr. Alice (Farnham)",female,49,0,0,17465,25.9292,D17,S
798,1,3,"Osman, Mrs. Mara",female,31,0,0,349244,8.6833,,S
799,0,3,"Ibrahim Shawah, Mr. Yousseff",male,30,0,0,2685,7.2292,,C
800,0,3,"Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)",female,30,1,1,345773,24.15,,S
801,0,2,"Ponesell, Mr. Martin",male,34,0,0,250647,13,,S
802,1,2,"Collyer, Mrs. Harvey (Charlotte Annie Tate)",female,31,1,1,C.A. 31921,26.25,,S
803,1,1,"Carter, Master. William Thornton II",male,11,1,2,113760,120,B96 B98,S
804,1,3,"Thomas, Master. Assad Alexander",male,0.42,0,1,2625,8.5167,,C
805,1,3,"Hedman, Mr. Oskar Arvid",male,27,0,0,347089,6.975,,S
806,0,3,"Johansson, Mr. Karl Johan",male,31,0,0,347063,7.775,,S
807,0,1,"Andrews, Mr. Thomas Jr",male,39,0,0,112050,0,A36,S
808,0,3,"Pettersson, Miss. Ellen Natalia",female,18,0,0,347087,7.775,,S
809,0,2,"Meyer, Mr. August",male,39,0,0,248723,13,,S
810,1,1,"Chambers, Mrs. Norman Campbell (Bertha Griggs)",female,33,1,0,113806,53.1,E8,S
811,0,3,"Alexander, Mr. William",male,26,0,0,3474,7.8875,,S
812,0,3,"Lester, Mr. James",male,39,0,0,A/4 48871,24.15,,S
813,0,2,"Slemen, Mr. Richard James",male,35,0,0,28206,10.5,,S
814,0,3,"Andersson, Miss. Ebba Iris Alfrida",female,6,4,2,347082,31.275,,S
815,0,3,"Tomlin, Mr. Ernest Portage",male,30.5,0,0,364499,8.05,,S
816,0,1,"Fry, Mr. Richard",male,,0,0,112058,0,B102,S
817,0,3,"Heininen, Miss. Wendla Maria",female,23,0,0,STON/O2. 3101290,7.925,,S
818,0,2,"Mallet, Mr. Albert",male,31,1,1,S.C./PARIS 2079,37.0042,,C
819,0,3,"Holm, Mr. John Fredrik Alexander",male,43,0,0,C 7075,6.45,,S
820,0,3,"Skoog, Master. Karl Thorsten",male,10,3,2,347088,27.9,,S
821,1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52,1,1,12749,93.5,B69,S
822,1,3,"Lulic, Mr. Nikola",male,27,0,0,315098,8.6625,,S
823,0,1,"Reuchlin, Jonkheer. John George",male,38,0,0,19972,0,,S
824,1,3,"Moor, Mrs. (Beila)",female,27,0,1,392096,12.475,E121,S
825,0,3,"Panula, Master. Urho Abraham",male,2,4,1,3101295,39.6875,,S
826,0,3,"Flynn, Mr. John",male,,0,0,368323,6.95,,Q
827,0,3,"Lam, Mr. Len",male,,0,0,1601,56.4958,,S
828,1,2,"Mallet, Master. Andre",male,1,0,2,S.C./PARIS 2079,37.0042,,C
829,1,3,"McCormack, Mr. Thomas Joseph",male,,0,0,367228,7.75,,Q
830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62,0,0,113572,80,B28,
831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15,1,0,2659,14.4542,,C
832,1,2,"Richards, Master. George Sibley",male,0.83,1,1,29106,18.75,,S
833,0,3,"Saad, Mr. Amin",male,,0,0,2671,7.2292,,C
834,0,3,"Augustsson, Mr. Albert",male,23,0,0,347468,7.8542,,S
835,0,3,"Allum, Mr. Owen George",male,18,0,0,2223,8.3,,S
836,1,1,"Compton, Miss. Sara Rebecca",female,39,1,1,PC 17756,83.1583,E49,C
837,0,3,"Pasic, Mr. Jakob",male,21,0,0,315097,8.6625,,S
838,0,3,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S
839,1,3,"Chip, Mr. Chang",male,32,0,0,1601,56.4958,,S
840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C
841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20,0,0,SOTON/O2 3101287,7.925,,S
842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S
843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C
844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C
845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S
846,0,3,"Abbing, Mr. Anthony",male,42,0,0,C.A. 5547,7.55,,S
847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S
848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C
849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S
850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C
851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S
852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S
853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C
854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S
855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S
856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S
857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S
858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S
859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C
860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C
861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S
862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S
863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S
864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S
865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S
866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S
867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C
868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S
869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S
870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S
871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S
872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S
873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S
874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S
875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C
876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S
878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S
879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q
1 PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
2 1 0 3 Braund, Mr. Owen Harris male 22 1 0 A/5 21171 7.25 S
3 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0 PC 17599 71.2833 C85 C
4 3 1 3 Heikkinen, Miss. Laina female 26 0 0 STON/O2. 3101282 7.925 S
5 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0 113803 53.1 C123 S
6 5 0 3 Allen, Mr. William Henry male 35 0 0 373450 8.05 S
7 6 0 3 Moran, Mr. James male 0 0 330877 8.4583 Q
8 7 0 1 McCarthy, Mr. Timothy J male 54 0 0 17463 51.8625 E46 S
9 8 0 3 Palsson, Master. Gosta Leonard male 2 3 1 349909 21.075 S
10 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27 0 2 347742 11.1333 S
11 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14 1 0 237736 30.0708 C
12 11 1 3 Sandstrom, Miss. Marguerite Rut female 4 1 1 PP 9549 16.7 G6 S
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628 627 0 2 Kirkland, Rev. Charles Leonard male 57 0 0 219533 12.35 Q
629 628 1 1 Longley, Miss. Gretchen Fiske female 21 0 0 13502 77.9583 D9 S
630 629 0 3 Bostandyeff, Mr. Guentcho male 26 0 0 349224 7.8958 S
631 630 0 3 O'Connell, Mr. Patrick D male 0 0 334912 7.7333 Q
632 631 1 1 Barkworth, Mr. Algernon Henry Wilson male 80 0 0 27042 30 A23 S
633 632 0 3 Lundahl, Mr. Johan Svensson male 51 0 0 347743 7.0542 S
634 633 1 1 Stahelin-Maeglin, Dr. Max male 32 0 0 13214 30.5 B50 C
635 634 0 1 Parr, Mr. William Henry Marsh male 0 0 112052 0 S
636 635 0 3 Skoog, Miss. Mabel female 9 3 2 347088 27.9 S
637 636 1 2 Davis, Miss. Mary female 28 0 0 237668 13 S
638 637 0 3 Leinonen, Mr. Antti Gustaf male 32 0 0 STON/O 2. 3101292 7.925 S
639 638 0 2 Collyer, Mr. Harvey male 31 1 1 C.A. 31921 26.25 S
640 639 0 3 Panula, Mrs. Juha (Maria Emilia Ojala) female 41 0 5 3101295 39.6875 S
641 640 0 3 Thorneycroft, Mr. Percival male 1 0 376564 16.1 S
642 641 0 3 Jensen, Mr. Hans Peder male 20 0 0 350050 7.8542 S
643 642 1 1 Sagesser, Mlle. Emma female 24 0 0 PC 17477 69.3 B35 C
644 643 0 3 Skoog, Miss. Margit Elizabeth female 2 3 2 347088 27.9 S
645 644 1 3 Foo, Mr. Choong male 0 0 1601 56.4958 S
646 645 1 3 Baclini, Miss. Eugenie female 0.75 2 1 2666 19.2583 C
647 646 1 1 Harper, Mr. Henry Sleeper male 48 1 0 PC 17572 76.7292 D33 C
648 647 0 3 Cor, Mr. Liudevit male 19 0 0 349231 7.8958 S
649 648 1 1 Simonius-Blumer, Col. Oberst Alfons male 56 0 0 13213 35.5 A26 C
650 649 0 3 Willey, Mr. Edward male 0 0 S.O./P.P. 751 7.55 S
651 650 1 3 Stanley, Miss. Amy Zillah Elsie female 23 0 0 CA. 2314 7.55 S
652 651 0 3 Mitkoff, Mr. Mito male 0 0 349221 7.8958 S
653 652 1 2 Doling, Miss. Elsie female 18 0 1 231919 23 S
654 653 0 3 Kalvik, Mr. Johannes Halvorsen male 21 0 0 8475 8.4333 S
655 654 1 3 O'Leary, Miss. Hanora "Norah" female 0 0 330919 7.8292 Q
656 655 0 3 Hegarty, Miss. Hanora "Nora" female 18 0 0 365226 6.75 Q
657 656 0 2 Hickman, Mr. Leonard Mark male 24 2 0 S.O.C. 14879 73.5 S
658 657 0 3 Radeff, Mr. Alexander male 0 0 349223 7.8958 S
659 658 0 3 Bourke, Mrs. John (Catherine) female 32 1 1 364849 15.5 Q
660 659 0 2 Eitemiller, Mr. George Floyd male 23 0 0 29751 13 S
661 660 0 1 Newell, Mr. Arthur Webster male 58 0 2 35273 113.275 D48 C
662 661 1 1 Frauenthal, Dr. Henry William male 50 2 0 PC 17611 133.65 S
663 662 0 3 Badt, Mr. Mohamed male 40 0 0 2623 7.225 C
664 663 0 1 Colley, Mr. Edward Pomeroy male 47 0 0 5727 25.5875 E58 S
665 664 0 3 Coleff, Mr. Peju male 36 0 0 349210 7.4958 S
666 665 1 3 Lindqvist, Mr. Eino William male 20 1 0 STON/O 2. 3101285 7.925 S
667 666 0 2 Hickman, Mr. Lewis male 32 2 0 S.O.C. 14879 73.5 S
668 667 0 2 Butler, Mr. Reginald Fenton male 25 0 0 234686 13 S
669 668 0 3 Rommetvedt, Mr. Knud Paust male 0 0 312993 7.775 S
670 669 0 3 Cook, Mr. Jacob male 43 0 0 A/5 3536 8.05 S
671 670 1 1 Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright) female 1 0 19996 52 C126 S
672 671 1 2 Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford) female 40 1 1 29750 39 S
673 672 0 1 Davidson, Mr. Thornton male 31 1 0 F.C. 12750 52 B71 S
674 673 0 2 Mitchell, Mr. Henry Michael male 70 0 0 C.A. 24580 10.5 S
675 674 1 2 Wilhelms, Mr. Charles male 31 0 0 244270 13 S
676 675 0 2 Watson, Mr. Ennis Hastings male 0 0 239856 0 S
677 676 0 3 Edvardsson, Mr. Gustaf Hjalmar male 18 0 0 349912 7.775 S
678 677 0 3 Sawyer, Mr. Frederick Charles male 24.5 0 0 342826 8.05 S
679 678 1 3 Turja, Miss. Anna Sofia female 18 0 0 4138 9.8417 S
680 679 0 3 Goodwin, Mrs. Frederick (Augusta Tyler) female 43 1 6 CA 2144 46.9 S
681 680 1 1 Cardeza, Mr. Thomas Drake Martinez male 36 0 1 PC 17755 512.3292 B51 B53 B55 C
682 681 0 3 Peters, Miss. Katie female 0 0 330935 8.1375 Q
683 682 1 1 Hassab, Mr. Hammad male 27 0 0 PC 17572 76.7292 D49 C
684 683 0 3 Olsvigen, Mr. Thor Anderson male 20 0 0 6563 9.225 S
685 684 0 3 Goodwin, Mr. Charles Edward male 14 5 2 CA 2144 46.9 S
686 685 0 2 Brown, Mr. Thomas William Solomon male 60 1 1 29750 39 S
687 686 0 2 Laroche, Mr. Joseph Philippe Lemercier male 25 1 2 SC/Paris 2123 41.5792 C
688 687 0 3 Panula, Mr. Jaako Arnold male 14 4 1 3101295 39.6875 S
689 688 0 3 Dakic, Mr. Branko male 19 0 0 349228 10.1708 S
690 689 0 3 Fischer, Mr. Eberhard Thelander male 18 0 0 350036 7.7958 S
691 690 1 1 Madill, Miss. Georgette Alexandra female 15 0 1 24160 211.3375 B5 S
692 691 1 1 Dick, Mr. Albert Adrian male 31 1 0 17474 57 B20 S
693 692 1 3 Karun, Miss. Manca female 4 0 1 349256 13.4167 C
694 693 1 3 Lam, Mr. Ali male 0 0 1601 56.4958 S
695 694 0 3 Saad, Mr. Khalil male 25 0 0 2672 7.225 C
696 695 0 1 Weir, Col. John male 60 0 0 113800 26.55 S
697 696 0 2 Chapman, Mr. Charles Henry male 52 0 0 248731 13.5 S
698 697 0 3 Kelly, Mr. James male 44 0 0 363592 8.05 S
699 698 1 3 Mullens, Miss. Katherine "Katie" female 0 0 35852 7.7333 Q
700 699 0 1 Thayer, Mr. John Borland male 49 1 1 17421 110.8833 C68 C
701 700 0 3 Humblen, Mr. Adolf Mathias Nicolai Olsen male 42 0 0 348121 7.65 F G63 S
702 701 1 1 Astor, Mrs. John Jacob (Madeleine Talmadge Force) female 18 1 0 PC 17757 227.525 C62 C64 C
703 702 1 1 Silverthorne, Mr. Spencer Victor male 35 0 0 PC 17475 26.2875 E24 S
704 703 0 3 Barbara, Miss. Saiide female 18 0 1 2691 14.4542 C
705 704 0 3 Gallagher, Mr. Martin male 25 0 0 36864 7.7417 Q
706 705 0 3 Hansen, Mr. Henrik Juul male 26 1 0 350025 7.8542 S
707 706 0 2 Morley, Mr. Henry Samuel ("Mr Henry Marshall") male 39 0 0 250655 26 S
708 707 1 2 Kelly, Mrs. Florence "Fannie" female 45 0 0 223596 13.5 S
709 708 1 1 Calderhead, Mr. Edward Pennington male 42 0 0 PC 17476 26.2875 E24 S
710 709 1 1 Cleaver, Miss. Alice female 22 0 0 113781 151.55 S
711 710 1 3 Moubarek, Master. Halim Gonios ("William George") male 1 1 2661 15.2458 C
712 711 1 1 Mayne, Mlle. Berthe Antonine ("Mrs de Villiers") female 24 0 0 PC 17482 49.5042 C90 C
713 712 0 1 Klaber, Mr. Herman male 0 0 113028 26.55 C124 S
714 713 1 1 Taylor, Mr. Elmer Zebley male 48 1 0 19996 52 C126 S
715 714 0 3 Larsson, Mr. August Viktor male 29 0 0 7545 9.4833 S
716 715 0 2 Greenberg, Mr. Samuel male 52 0 0 250647 13 S
717 716 0 3 Soholt, Mr. Peter Andreas Lauritz Andersen male 19 0 0 348124 7.65 F G73 S
718 717 1 1 Endres, Miss. Caroline Louise female 38 0 0 PC 17757 227.525 C45 C
719 718 1 2 Troutt, Miss. Edwina Celia "Winnie" female 27 0 0 34218 10.5 E101 S
720 719 0 3 McEvoy, Mr. Michael male 0 0 36568 15.5 Q
721 720 0 3 Johnson, Mr. Malkolm Joackim male 33 0 0 347062 7.775 S
722 721 1 2 Harper, Miss. Annie Jessie "Nina" female 6 0 1 248727 33 S
723 722 0 3 Jensen, Mr. Svend Lauritz male 17 1 0 350048 7.0542 S
724 723 0 2 Gillespie, Mr. William Henry male 34 0 0 12233 13 S
725 724 0 2 Hodges, Mr. Henry Price male 50 0 0 250643 13 S
726 725 1 1 Chambers, Mr. Norman Campbell male 27 1 0 113806 53.1 E8 S
727 726 0 3 Oreskovic, Mr. Luka male 20 0 0 315094 8.6625 S
728 727 1 2 Renouf, Mrs. Peter Henry (Lillian Jefferys) female 30 3 0 31027 21 S
729 728 1 3 Mannion, Miss. Margareth female 0 0 36866 7.7375 Q
730 729 0 2 Bryhl, Mr. Kurt Arnold Gottfrid male 25 1 0 236853 26 S
731 730 0 3 Ilmakangas, Miss. Pieta Sofia female 25 1 0 STON/O2. 3101271 7.925 S
732 731 1 1 Allen, Miss. Elisabeth Walton female 29 0 0 24160 211.3375 B5 S
733 732 0 3 Hassan, Mr. Houssein G N male 11 0 0 2699 18.7875 C
734 733 0 2 Knight, Mr. Robert J male 0 0 239855 0 S
735 734 0 2 Berriman, Mr. William John male 23 0 0 28425 13 S
736 735 0 2 Troupiansky, Mr. Moses Aaron male 23 0 0 233639 13 S
737 736 0 3 Williams, Mr. Leslie male 28.5 0 0 54636 16.1 S
738 737 0 3 Ford, Mrs. Edward (Margaret Ann Watson) female 48 1 3 W./C. 6608 34.375 S
739 738 1 1 Lesurer, Mr. Gustave J male 35 0 0 PC 17755 512.3292 B101 C
740 739 0 3 Ivanoff, Mr. Kanio male 0 0 349201 7.8958 S
741 740 0 3 Nankoff, Mr. Minko male 0 0 349218 7.8958 S
742 741 1 1 Hawksford, Mr. Walter James male 0 0 16988 30 D45 S
743 742 0 1 Cavendish, Mr. Tyrell William male 36 1 0 19877 78.85 C46 S
744 743 1 1 Ryerson, Miss. Susan Parker "Suzette" female 21 2 2 PC 17608 262.375 B57 B59 B63 B66 C
745 744 0 3 McNamee, Mr. Neal male 24 1 0 376566 16.1 S
746 745 1 3 Stranden, Mr. Juho male 31 0 0 STON/O 2. 3101288 7.925 S
747 746 0 1 Crosby, Capt. Edward Gifford male 70 1 1 WE/P 5735 71 B22 S
748 747 0 3 Abbott, Mr. Rossmore Edward male 16 1 1 C.A. 2673 20.25 S
749 748 1 2 Sinkkonen, Miss. Anna female 30 0 0 250648 13 S
750 749 0 1 Marvin, Mr. Daniel Warner male 19 1 0 113773 53.1 D30 S
751 750 0 3 Connaghton, Mr. Michael male 31 0 0 335097 7.75 Q
752 751 1 2 Wells, Miss. Joan female 4 1 1 29103 23 S
753 752 1 3 Moor, Master. Meier male 6 0 1 392096 12.475 E121 S
754 753 0 3 Vande Velde, Mr. Johannes Joseph male 33 0 0 345780 9.5 S
755 754 0 3 Jonkoff, Mr. Lalio male 23 0 0 349204 7.8958 S
756 755 1 2 Herman, Mrs. Samuel (Jane Laver) female 48 1 2 220845 65 S
757 756 1 2 Hamalainen, Master. Viljo male 0.67 1 1 250649 14.5 S
758 757 0 3 Carlsson, Mr. August Sigfrid male 28 0 0 350042 7.7958 S
759 758 0 2 Bailey, Mr. Percy Andrew male 18 0 0 29108 11.5 S
760 759 0 3 Theobald, Mr. Thomas Leonard male 34 0 0 363294 8.05 S
761 760 1 1 Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards) female 33 0 0 110152 86.5 B77 S
762 761 0 3 Garfirth, Mr. John male 0 0 358585 14.5 S
763 762 0 3 Nirva, Mr. Iisakki Antino Aijo male 41 0 0 SOTON/O2 3101272 7.125 S
764 763 1 3 Barah, Mr. Hanna Assi male 20 0 0 2663 7.2292 C
765 764 1 1 Carter, Mrs. William Ernest (Lucile Polk) female 36 1 2 113760 120 B96 B98 S
766 765 0 3 Eklund, Mr. Hans Linus male 16 0 0 347074 7.775 S
767 766 1 1 Hogeboom, Mrs. John C (Anna Andrews) female 51 1 0 13502 77.9583 D11 S
768 767 0 1 Brewe, Dr. Arthur Jackson male 0 0 112379 39.6 C
769 768 0 3 Mangan, Miss. Mary female 30.5 0 0 364850 7.75 Q
770 769 0 3 Moran, Mr. Daniel J male 1 0 371110 24.15 Q
771 770 0 3 Gronnestad, Mr. Daniel Danielsen male 32 0 0 8471 8.3625 S
772 771 0 3 Lievens, Mr. Rene Aime male 24 0 0 345781 9.5 S
773 772 0 3 Jensen, Mr. Niels Peder male 48 0 0 350047 7.8542 S
774 773 0 2 Mack, Mrs. (Mary) female 57 0 0 S.O./P.P. 3 10.5 E77 S
775 774 0 3 Elias, Mr. Dibo male 0 0 2674 7.225 C
776 775 1 2 Hocking, Mrs. Elizabeth (Eliza Needs) female 54 1 3 29105 23 S
777 776 0 3 Myhrman, Mr. Pehr Fabian Oliver Malkolm male 18 0 0 347078 7.75 S
778 777 0 3 Tobin, Mr. Roger male 0 0 383121 7.75 F38 Q
779 778 1 3 Emanuel, Miss. Virginia Ethel female 5 0 0 364516 12.475 S
780 779 0 3 Kilgannon, Mr. Thomas J male 0 0 36865 7.7375 Q
781 780 1 1 Robert, Mrs. Edward Scott (Elisabeth Walton McMillan) female 43 0 1 24160 211.3375 B3 S
782 781 1 3 Ayoub, Miss. Banoura female 13 0 0 2687 7.2292 C
783 782 1 1 Dick, Mrs. Albert Adrian (Vera Gillespie) female 17 1 0 17474 57 B20 S
784 783 0 1 Long, Mr. Milton Clyde male 29 0 0 113501 30 D6 S
785 784 0 3 Johnston, Mr. Andrew G male 1 2 W./C. 6607 23.45 S
786 785 0 3 Ali, Mr. William male 25 0 0 SOTON/O.Q. 3101312 7.05 S
787 786 0 3 Harmer, Mr. Abraham (David Lishin) male 25 0 0 374887 7.25 S
788 787 1 3 Sjoblom, Miss. Anna Sofia female 18 0 0 3101265 7.4958 S
789 788 0 3 Rice, Master. George Hugh male 8 4 1 382652 29.125 Q
790 789 1 3 Dean, Master. Bertram Vere male 1 1 2 C.A. 2315 20.575 S
791 790 0 1 Guggenheim, Mr. Benjamin male 46 0 0 PC 17593 79.2 B82 B84 C
792 791 0 3 Keane, Mr. Andrew "Andy" male 0 0 12460 7.75 Q
793 792 0 2 Gaskell, Mr. Alfred male 16 0 0 239865 26 S
794 793 0 3 Sage, Miss. Stella Anna female 8 2 CA. 2343 69.55 S
795 794 0 1 Hoyt, Mr. William Fisher male 0 0 PC 17600 30.6958 C
796 795 0 3 Dantcheff, Mr. Ristiu male 25 0 0 349203 7.8958 S
797 796 0 2 Otter, Mr. Richard male 39 0 0 28213 13 S
798 797 1 1 Leader, Dr. Alice (Farnham) female 49 0 0 17465 25.9292 D17 S
799 798 1 3 Osman, Mrs. Mara female 31 0 0 349244 8.6833 S
800 799 0 3 Ibrahim Shawah, Mr. Yousseff male 30 0 0 2685 7.2292 C
801 800 0 3 Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert) female 30 1 1 345773 24.15 S
802 801 0 2 Ponesell, Mr. Martin male 34 0 0 250647 13 S
803 802 1 2 Collyer, Mrs. Harvey (Charlotte Annie Tate) female 31 1 1 C.A. 31921 26.25 S
804 803 1 1 Carter, Master. William Thornton II male 11 1 2 113760 120 B96 B98 S
805 804 1 3 Thomas, Master. Assad Alexander male 0.42 0 1 2625 8.5167 C
806 805 1 3 Hedman, Mr. Oskar Arvid male 27 0 0 347089 6.975 S
807 806 0 3 Johansson, Mr. Karl Johan male 31 0 0 347063 7.775 S
808 807 0 1 Andrews, Mr. Thomas Jr male 39 0 0 112050 0 A36 S
809 808 0 3 Pettersson, Miss. Ellen Natalia female 18 0 0 347087 7.775 S
810 809 0 2 Meyer, Mr. August male 39 0 0 248723 13 S
811 810 1 1 Chambers, Mrs. Norman Campbell (Bertha Griggs) female 33 1 0 113806 53.1 E8 S
812 811 0 3 Alexander, Mr. William male 26 0 0 3474 7.8875 S
813 812 0 3 Lester, Mr. James male 39 0 0 A/4 48871 24.15 S
814 813 0 2 Slemen, Mr. Richard James male 35 0 0 28206 10.5 S
815 814 0 3 Andersson, Miss. Ebba Iris Alfrida female 6 4 2 347082 31.275 S
816 815 0 3 Tomlin, Mr. Ernest Portage male 30.5 0 0 364499 8.05 S
817 816 0 1 Fry, Mr. Richard male 0 0 112058 0 B102 S
818 817 0 3 Heininen, Miss. Wendla Maria female 23 0 0 STON/O2. 3101290 7.925 S
819 818 0 2 Mallet, Mr. Albert male 31 1 1 S.C./PARIS 2079 37.0042 C
820 819 0 3 Holm, Mr. John Fredrik Alexander male 43 0 0 C 7075 6.45 S
821 820 0 3 Skoog, Master. Karl Thorsten male 10 3 2 347088 27.9 S
822 821 1 1 Hays, Mrs. Charles Melville (Clara Jennings Gregg) female 52 1 1 12749 93.5 B69 S
823 822 1 3 Lulic, Mr. Nikola male 27 0 0 315098 8.6625 S
824 823 0 1 Reuchlin, Jonkheer. John George male 38 0 0 19972 0 S
825 824 1 3 Moor, Mrs. (Beila) female 27 0 1 392096 12.475 E121 S
826 825 0 3 Panula, Master. Urho Abraham male 2 4 1 3101295 39.6875 S
827 826 0 3 Flynn, Mr. John male 0 0 368323 6.95 Q
828 827 0 3 Lam, Mr. Len male 0 0 1601 56.4958 S
829 828 1 2 Mallet, Master. Andre male 1 0 2 S.C./PARIS 2079 37.0042 C
830 829 1 3 McCormack, Mr. Thomas Joseph male 0 0 367228 7.75 Q
831 830 1 1 Stone, Mrs. George Nelson (Martha Evelyn) female 62 0 0 113572 80 B28
832 831 1 3 Yasbeck, Mrs. Antoni (Selini Alexander) female 15 1 0 2659 14.4542 C
833 832 1 2 Richards, Master. George Sibley male 0.83 1 1 29106 18.75 S
834 833 0 3 Saad, Mr. Amin male 0 0 2671 7.2292 C
835 834 0 3 Augustsson, Mr. Albert male 23 0 0 347468 7.8542 S
836 835 0 3 Allum, Mr. Owen George male 18 0 0 2223 8.3 S
837 836 1 1 Compton, Miss. Sara Rebecca female 39 1 1 PC 17756 83.1583 E49 C
838 837 0 3 Pasic, Mr. Jakob male 21 0 0 315097 8.6625 S
839 838 0 3 Sirota, Mr. Maurice male 0 0 392092 8.05 S
840 839 1 3 Chip, Mr. Chang male 32 0 0 1601 56.4958 S
841 840 1 1 Marechal, Mr. Pierre male 0 0 11774 29.7 C47 C
842 841 0 3 Alhomaki, Mr. Ilmari Rudolf male 20 0 0 SOTON/O2 3101287 7.925 S
843 842 0 2 Mudd, Mr. Thomas Charles male 16 0 0 S.O./P.P. 3 10.5 S
844 843 1 1 Serepeca, Miss. Augusta female 30 0 0 113798 31 C
845 844 0 3 Lemberopolous, Mr. Peter L male 34.5 0 0 2683 6.4375 C
846 845 0 3 Culumovic, Mr. Jeso male 17 0 0 315090 8.6625 S
847 846 0 3 Abbing, Mr. Anthony male 42 0 0 C.A. 5547 7.55 S
848 847 0 3 Sage, Mr. Douglas Bullen male 8 2 CA. 2343 69.55 S
849 848 0 3 Markoff, Mr. Marin male 35 0 0 349213 7.8958 C
850 849 0 2 Harper, Rev. John male 28 0 1 248727 33 S
851 850 1 1 Goldenberg, Mrs. Samuel L (Edwiga Grabowska) female 1 0 17453 89.1042 C92 C
852 851 0 3 Andersson, Master. Sigvard Harald Elias male 4 4 2 347082 31.275 S
853 852 0 3 Svensson, Mr. Johan male 74 0 0 347060 7.775 S
854 853 0 3 Boulos, Miss. Nourelain female 9 1 1 2678 15.2458 C
855 854 1 1 Lines, Miss. Mary Conover female 16 0 1 PC 17592 39.4 D28 S
856 855 0 2 Carter, Mrs. Ernest Courtenay (Lilian Hughes) female 44 1 0 244252 26 S
857 856 1 3 Aks, Mrs. Sam (Leah Rosen) female 18 0 1 392091 9.35 S
858 857 1 1 Wick, Mrs. George Dennick (Mary Hitchcock) female 45 1 1 36928 164.8667 S
859 858 1 1 Daly, Mr. Peter Denis male 51 0 0 113055 26.55 E17 S
860 859 1 3 Baclini, Mrs. Solomon (Latifa Qurban) female 24 0 3 2666 19.2583 C
861 860 0 3 Razi, Mr. Raihed male 0 0 2629 7.2292 C
862 861 0 3 Hansen, Mr. Claus Peter male 41 2 0 350026 14.1083 S
863 862 0 2 Giles, Mr. Frederick Edward male 21 1 0 28134 11.5 S
864 863 1 1 Swift, Mrs. Frederick Joel (Margaret Welles Barron) female 48 0 0 17466 25.9292 D17 S
865 864 0 3 Sage, Miss. Dorothy Edith "Dolly" female 8 2 CA. 2343 69.55 S
866 865 0 2 Gill, Mr. John William male 24 0 0 233866 13 S
867 866 1 2 Bystrom, Mrs. (Karolina) female 42 0 0 236852 13 S
868 867 1 2 Duran y More, Miss. Asuncion female 27 1 0 SC/PARIS 2149 13.8583 C
869 868 0 1 Roebling, Mr. Washington Augustus II male 31 0 0 PC 17590 50.4958 A24 S
870 869 0 3 van Melkebeke, Mr. Philemon male 0 0 345777 9.5 S
871 870 1 3 Johnson, Master. Harold Theodor male 4 1 1 347742 11.1333 S
872 871 0 3 Balkic, Mr. Cerin male 26 0 0 349248 7.8958 S
873 872 1 1 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47 1 1 11751 52.5542 D35 S
874 873 0 1 Carlsson, Mr. Frans Olof male 33 0 0 695 5 B51 B53 B55 S
875 874 0 3 Vander Cruyssen, Mr. Victor male 47 0 0 345765 9 S
876 875 1 2 Abelson, Mrs. Samuel (Hannah Wizosky) female 28 1 0 P/PP 3381 24 C
877 876 1 3 Najib, Miss. Adele Kiamie "Jane" female 15 0 0 2667 7.225 C
878 877 0 3 Gustafsson, Mr. Alfred Ossian male 20 0 0 7534 9.8458 S
879 878 0 3 Petroff, Mr. Nedelio male 19 0 0 349212 7.8958 S
880 879 0 3 Laleff, Mr. Kristo male 0 0 349217 7.8958 S
881 880 1 1 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56 0 1 11767 83.1583 C50 C
882 881 1 2 Shelley, Mrs. William (Imanita Parrish Hall) female 25 0 1 230433 26 S
883 882 0 3 Markun, Mr. Johann male 33 0 0 349257 7.8958 S
884 883 0 3 Dahlberg, Miss. Gerda Ulrika female 22 0 0 7552 10.5167 S
885 884 0 2 Banfield, Mr. Frederick James male 28 0 0 C.A./SOTON 34068 10.5 S
886 885 0 3 Sutehall, Mr. Henry Jr male 25 0 0 SOTON/OQ 392076 7.05 S
887 886 0 3 Rice, Mrs. William (Margaret Norton) female 39 0 5 382652 29.125 Q
888 887 0 2 Montvila, Rev. Juozas male 27 0 0 211536 13 S
889 888 1 1 Graham, Miss. Margaret Edith female 19 0 0 112053 30 B42 S
890 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female 1 2 W./C. 6607 23.45 S
891 890 1 1 Behr, Mr. Karl Howell male 26 0 0 111369 30 C148 C
892 891 0 3 Dooley, Mr. Patrick male 32 0 0 370376 7.75 Q

6575
实验 探索Chipotle快餐数据/data/wind.csv Normal file
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File diff suppressed because it is too large Load Diff

4
梯度下降/最简单函数求极值.py
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@ -16,8 +16,8 @@ def plotf(loss):
plt.show()
def main():
x = 15
lr = 1.1
x = 1000
lr = 0.9
steps = 400
loss = []
for i in range(steps):