diff --git a/实验 Pandas_速查表/Pandas_速查表.pdf b/实验 Pandas_速查表/Pandas_速查表.pdf
new file mode 100644
index 0000000..3582e0c
Binary files /dev/null and b/实验 Pandas_速查表/Pandas_速查表.pdf differ
diff --git a/实验 Pandas_速查表/agg.py b/实验 Pandas_速查表/agg.py
new file mode 100644
index 0000000..33e4eb9
--- /dev/null
+++ b/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)
diff --git a/实验 Pandas_速查表/concat.py b/实验 Pandas_速查表/concat.py
new file mode 100644
index 0000000..eac199a
--- /dev/null
+++ b/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)
\ No newline at end of file
diff --git a/实验 Pandas_速查表/groupby.py b/实验 Pandas_速查表/groupby.py
new file mode 100644
index 0000000..e1b2f29
--- /dev/null
+++ b/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
diff --git a/实验 Pandas_速查表/merge.py b/实验 Pandas_速查表/merge.py
new file mode 100644
index 0000000..975a6bc
--- /dev/null
+++ b/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)
diff --git a/实验 Pandas_速查表/pivot.py b/实验 Pandas_速查表/pivot.py
new file mode 100644
index 0000000..8b87fd2
--- /dev/null
+++ b/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)
diff --git a/实验1/1.py b/实验 美国各州的统计数据 及美国人的生日/1.py
similarity index 100%
rename from 实验1/1.py
rename to 实验 美国各州的统计数据 及美国人的生日/1.py
diff --git a/实验1/births.csv b/实验 美国各州的统计数据 及美国人的生日/births.csv
similarity index 100%
rename from 实验1/births.csv
rename to 实验 美国各州的统计数据 及美国人的生日/births.csv
diff --git a/实验1/pands速查表.py b/实验 美国各州的统计数据 及美国人的生日/pands速查表.py
similarity index 100%
rename from 实验1/pands速查表.py
rename to 实验 美国各州的统计数据 及美国人的生日/pands速查表.py
diff --git a/实验1/state-abbrevs.csv b/实验 美国各州的统计数据 及美国人的生日/state-abbrevs.csv
similarity index 100%
rename from 实验1/state-abbrevs.csv
rename to 实验 美国各州的统计数据 及美国人的生日/state-abbrevs.csv
diff --git a/实验1/state-areas.csv b/实验 美国各州的统计数据 及美国人的生日/state-areas.csv
similarity index 100%
rename from 实验1/state-areas.csv
rename to 实验 美国各州的统计数据 及美国人的生日/state-areas.csv
diff --git a/实验1/state-population.csv b/实验 美国各州的统计数据 及美国人的生日/state-population.csv
similarity index 100%
rename from 实验1/state-population.csv
rename to 实验 美国各州的统计数据 及美国人的生日/state-population.csv
diff --git a/实验0/猪肉价格/bean.png b/实验0/猪肉价格/bean.png
index 1c739e1..2e5c9bd 100644
Binary files a/实验0/猪肉价格/bean.png and b/实验0/猪肉价格/bean.png differ
diff --git a/实验0/猪肉价格/bean.txt b/实验0/猪肉价格/bean.txt
index 545edf9..d96fb4c 100644
--- a/实验0/猪肉价格/bean.txt
+++ b/实验0/猪肉价格/bean.txt
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diff --git a/实验0/猪肉价格/maizeprice.png b/实验0/猪肉价格/maizeprice.png
index b438a15..fa56abe 100644
Binary files a/实验0/猪肉价格/maizeprice.png and b/实验0/猪肉价格/maizeprice.png differ
diff --git a/实验0/猪肉价格/maizeprice.txt b/实验0/猪肉价格/maizeprice.txt
index 24d000c..3e0ea67 100644
--- a/实验0/猪肉价格/maizeprice.txt
+++ b/实验0/猪肉价格/maizeprice.txt
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diff --git a/实验0/猪肉价格/pig_in.png b/实验0/猪肉价格/pig_in.png
index 0cfc205..7ab4b73 100644
Binary files a/实验0/猪肉价格/pig_in.png and b/实验0/猪肉价格/pig_in.png differ
diff --git a/实验0/猪肉价格/pig_in.txt b/实验0/猪肉价格/pig_in.txt
index 81f852d..df408e1 100644
--- a/实验0/猪肉价格/pig_in.txt
+++ b/实验0/猪肉价格/pig_in.txt
@@ -1,366 +1,366 @@
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diff --git a/实验0/猪肉价格/pig_local.png b/实验0/猪肉价格/pig_local.png
index c50b1ad..650e43a 100644
Binary files a/实验0/猪肉价格/pig_local.png and b/实验0/猪肉价格/pig_local.png differ
diff --git a/实验0/猪肉价格/pig_local.txt b/实验0/猪肉价格/pig_local.txt
index 6741353..ca483e5 100644
--- a/实验0/猪肉价格/pig_local.txt
+++ b/实验0/猪肉价格/pig_local.txt
@@ -1,366 +1,366 @@
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diff --git a/实验0/猪肉价格/pigprice.png b/实验0/猪肉价格/pigprice.png
index 9d144ff..3f9c5ce 100644
Binary files a/实验0/猪肉价格/pigprice.png and b/实验0/猪肉价格/pigprice.png differ
diff --git a/实验0/猪肉价格/pigprice.txt b/实验0/猪肉价格/pigprice.txt
index 49046bd..b79bd2f 100644
--- a/实验0/猪肉价格/pigprice.txt
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diff --git a/实验0/猪肉价格/time.txt b/实验0/猪肉价格/time.txt
index 82df947..e487ade 100644
--- a/实验0/猪肉价格/time.txt
+++ b/实验0/猪肉价格/time.txt
@@ -1,4 +1,4 @@
-20230122
-20221122
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