Using Pandas concat and merge to Combine Multiple Datasets

Objective: Learn how to use Pandas to merge multiple datasets.

Learning content includes the concat method and the merge method.

Code examples:

import pandas as pd
# create first dataset
data1 = {'姓名': ['张三', '李四', '王五'],
         '部门': ['销售部', '市场部', '技术部'],
         '销售额': [120, 150, 130]}
df1 = pd.DataFrame(data1)
print(f"第一个数据集: \n{df1}")

# create second dataset
data2 = {'姓名': ['赵六', '孙七', '周八'],
         '部门': ['财务部', '人力资源部', '销售部'],
         '销售额': [140, 160, 170]}
df2 = pd.DataFrame(data2)
print(f"第二个数据集: \n{df2}")

# concatenate rows
df_concat_rows = pd.concat([df1, df2])
print(f"按行合并后的数据集: \n{df_concat_rows}")

# concatenate columns with different columns
data3 = {'姓名': ['张三', '李四', '王五'],
         '成本': [80, 90, 100]}
df3 = pd.DataFrame(data3)
print(f"第三个数据集: \n{df3}")
df_concat_cols = pd.concat([df1, df3], axis=1)
print(f"按列合并后的数据集: \n{df_concat_cols}")

# concatenate with ignore_index
df_concat_index = pd.concat([df1, df2], ignore_index=True)
print(f"按行合并并指定索引后的数据集: \n{df_concat_index}")

# merge inner
df_merge_inner = pd.merge(df1, df2, on='姓名', how='inner')
print(f"内连接后的数据集: \n{df_merge_inner}")

# merge left
df_merge_left = pd.merge(df1, df2, on='姓名', how='left')
print(f"左连接后的数据集: \n{df_merge_left}")

# merge right
df_merge_right = pd.merge(df1, df2, on='姓名', how='right')
print(f"右连接后的数据集: \n{df_merge_right}")

# merge outer
df_merge_outer = pd.merge(df1, df2, on='姓名', how='outer')
print(f"外连接后的数据集: \n{df_merge_outer}")

# merge with multiple keys
data4 = {'姓名': ['张三', '李四', '王五'],
         '部门': ['销售部', '市场部', '技术部'],
         '成本': [80, 90, 100]}
df4 = pd.DataFrame(data4)
print(f"第四个数据集: \n{df4}")
df_merge_multi_key = pd.merge(df1, df4, on=['姓名', '部门'], how='inner')
print(f"指定多个键合并后的数据集: \n{df_merge_multi_key}")

Practice repeats the same steps to combine two datasets that share identical columns.

Summary: After this exercise you should be able to merge datasets with Pandas using both concat and merge , handling row‑wise, column‑wise, index handling, and multi‑key joins.