shao

实验 Pandas_速查表/agg.py

@@ -0,0 +1,13 @@
+
+import pandas as pd
+
+import plotly.express as px
+
+data = {
+    'Date': ['2024-01-01', '2024-01-01', '2024-01-02', '2024-01-02', '2024-01-03', '2024-01-03'],
+    'Product': ['A', 'B', 'A', 'B', 'A', 'B'],
+    'Sales': [100, 150, 120, 110, 130, 120]
+}
+df = pd.DataFrame(data)
+result = df.groupby('Product')['Sales'].agg(['sum', 'mean'])
+print(result)

实验 Pandas_速查表/concat.py

@@ -0,0 +1,23 @@
+
+import pandas as pd
+
+import plotly.express as px
+
+data1 = {
+    'Date': ['2024-01-01', '2024-01-02'],
+    'Product': ['A', 'B'],
+    'Sales': [100, 150]
+}
+df1 = pd.DataFrame(data1)
+
+data2 = {
+    'Date': ['2024-02-01', '2024-02-02'],
+    'Product': ['A', 'B'],
+    'Sales': [120, 130]
+}
+df2 = pd.DataFrame(data2)
+merged_df = pd.concat([df1, df2], ignore_index=True)
+print(merged_df)
+
+merged_df = pd.concat([df1, df2], axis=1)
+print(merged_df)

实验 Pandas_速查表/groupby.py

@@ -0,0 +1,15 @@
+
+import pandas as pd
+
+import plotly.express as px
+
+data = {
+    'Date': ['2024-01-01', '2024-01-01', '2024-01-02', '2024-01-02', '2024-01-03', '2024-01-03'],
+    'Product': ['A', 'B', 'A', 'B', 'A', 'B'],
+    'Sales': [100, 150, 120, 110, 130, 120]
+}
+df = pd.DataFrame(data)
+grouped_df = df.groupby('Product')['Sales'].sum()
+print(grouped_df)
+# 这个操作将按照Product列进行分组，并计算每个分组中Sales列的总和，从而得到每个产品的总销售额。
+# 这意味着产品A的总销售额为350，而产品B的总销售额为380。通过使用groupby方法，我们能够轻松地按照Product列对数据进行分组，并计算每个分组的Sales列总和，从而得到每个产品在所有日期上的总销售额。这个例子展示了groupby方法在数据分析和处理中的强大功能和灵活性

实验 Pandas_速查表/merge.py

@@ -0,0 +1,18 @@
+
+import pandas as pd
+
+import plotly.express as px
+
+products = pd.DataFrame({
+    'ProductID': [1, 2],
+    'ProductName': ['A', 'B']
+})
+
+sales = pd.DataFrame({
+    'TransactionID': [101, 102, 103],
+    'ProductID': [1, 2, 1],
+    'Sales': [100, 150, 120]
+})
+
+merged_df = pd.merge(sales, products, on='ProductID')
+print(merged_df)

实验 Pandas_速查表/pivot.py

@@ -0,0 +1,19 @@
+
+import pandas as pd
+
+import plotly.express as px
+
+df = px.data.iris()
+print(df.head(10))
+
+df1 = pd.melt(df)
+print(df1.tail(100))
+
+data = {
+    'Date': ['2024-01-01', '2024-01-01', '2024-01-02', '2024-01-02'],
+    'Product': ['A', 'B', 'A', 'B'],
+    'Sales': [100, 150, 120, 110]
+}
+df = pd.DataFrame(data)
+pivot_df = df.pivot(index='Date', columns='Product', values='Sales')
+print(pivot_df)

实验0/猪肉价格/bean.txt

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实验0/猪肉价格/maizeprice.txt

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实验0/猪肉价格/pig_in.txt

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实验0/猪肉价格/pig_local.txt

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实验0/猪肉价格/pigprice.txt

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实验0/猪肉价格/time.txt

@@ -1,4 +1,4 @@
-20230122
-20221122
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-20220221
+20240127
+20231127
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