极客教程Pandas中的列合并
在数据处理和分析中，经常需要将不同的数据集或数据片段合并成一个整体，以便进行更深入的分析。Pandas库提供了多种方式来实现数据的合并，其中concat()函数是一个非常强大的工具，用于沿着一定的轴将多个pandas对象合并到一起。本文将详细介绍如何使用Pandas的concat()函数来合并列，包括不同的参数和提供多个示例代码。
Pandas concat() 函数简介
concat()函数主要用于沿着一条轴将多个对象堆叠到一起。语法如下：
pandas.concat(objs, axis=0, join='outer', ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False, sort=False, copy=True)
objs: 一个序列或映射，这里面的每个对象都必须是一个Series或DataFrame。axis:{0/'index', 1/'columns'}，默认为0。沿着哪个轴进行连接。join:{'inner', 'outer'}，默认为’outer’。如何处理不同对象的索引轴。ignore_index: 布尔值，默认为False。如果为True，则不使用索引标签。keys: 序列，默认为None。如果传递了序列，则创建一个多级索引（层次化索引）。levels: 序列的序列，默认为None。用于构建MultiIndex的级别（仅在keys不为None时有效）。names: 列表，默认为None。用于创建层次化索引的级别名称（仅在keys不为None时有效）。verify_integrity: 布尔值，默认为False。检查新连接的轴是否包含重复项。sort: 布尔值，默认为False。在沿轴连接时，根据连接键对数据进行排序。copy: 布尔值，默认为True。如果为False，则在可能的情况下避免复制数据。
示例代码
示例1：基本的列合并
import pandas as pd
# 创建两个DataFrame
df1 = pd.DataFrame({
"A": ["A0", "A1", "A2", "A3"],
"B": ["B0", "B1", "B2", "B3"]
})
df2 = pd.DataFrame({
"C": ["C0", "C1", "C2", "C3"],
"D": ["D0", "D1", "D2", "D3"]
})
# 合并列
result = pd.concat([df1, df2], axis=1)
print(result)
Output:
示例2：使用ignore_index选项
import pandas as pd
# 创建两个DataFrame
df1 = pd.DataFrame({
"A": ["A0", "A1", "A2", "A3"],
"B": ["B0", "B1", "B2", "B3"]
}, index=[0, 1, 2, 3])
df2 = pd.DataFrame({
"C": ["C0", "C1", "C2", "C3"],
"D": ["D0", "D1", "D2", "D3"]
}, index=[2, 3, 4, 5])
# 合并列并忽略原有索引，创建新的索引
result = pd.concat([df1, df2], axis=1, ignore_index=True)
print(result)
Output:
示例3：使用keys创建多级索引
import pandas as pd
# 创建两个DataFrame
df1 = pd.DataFrame({
"A": ["A0", "A1", "A2", "A3"],
"B": ["B0", "B1", "B2", "B3"]
})
df2 = pd.DataFrame({
"C": ["C0", "C1", "C2", "C3"],
"D": ["D0", "D1", "D2", "D3"]
})
# 使用keys参数
result = pd.concat([df1, df2], axis=1, keys=['First', 'Second'])
print(result)
Output:
示例4：使用join进行内连接
import pandas as pd
# 创建两个DataFrame
df1 = pd.DataFrame({
"A": ["A0", "A1", "A2", "A3"],
"B": ["B0", "B1", "B2", "B3"]
}, index=[0, 1, 2, 3])
df2 = pd.DataFrame({
"B": ["B2", "B3", "B4", "B5"],
"C": ["C2", "C3", "C4", "C5"]
}, index=[2, 3, 4, 5])
# 内连接
result = pd.concat([df1, df2], axis=1, join='inner')
print(result)
Output:
示例5：验证数据完整性
import pandas as pd
# 创建两个DataFrame
df1 = pd.DataFrame({
"A": ["A0", "A1", "A2", "A3"],
"B": ["B0", "B1", "B2", "B3"]
})
df2 = pd.DataFrame({
"A": ["A4", "A5", "A6", "A7"],
"B": ["B4", "B5", "B6", "B7"]
})
# 尝试合并并检查数据完整性
try:
result = pd.concat([df1, df2], axis=1, verify_integrity=True)
print(result)
except ValueError as e:
print("Error:", e)
Output:
示例6：不复制数据
import pandas as pd
# 创建两个DataFrame
df1 = pd.DataFrame({
"A": ["A0", "A1", "A2", "A3"],
"B": ["B0", "B1", "B2", "B3"]
})
df2 = pd.DataFrame({
"C": ["C0", "C1", "C2", "C3"],
"D": ["D0", "D1", "D2", "D3"]
})
# 合并列，尽量不复制数据
result = pd.concat([df1, df2], axis=1, copy=False)
print(result)
Output:
示例7：排序数据
import pandas as pd
# 创建两个DataFrame
df1 = pd.DataFrame({
"B": ["B2", "B1", "B3", "B0"],
"A": ["A2", "A1", "A3", "A0"]
})
df2 = pd.DataFrame({
"D": ["D2", "D1", "D3", "D0"],
"C": ["C2", "C1", "C3", "C0"]
})
# 合并列并排序
result = pd.concat([df1, df2], axis=1, sort=True)
print(result)
Output:
示例8：合并多个DataFrame
import pandas as pd
# 创建多个DataFrame
df1 = pd.DataFrame({
"A": ["A0", "A1", "A2", "A3"],
"B": ["B0", "B1", "B2", "B3"]
})
df2 = pd.DataFrame({
"C": ["C0", "C1", "C2", "C3"],
"D": ["D0", "D1", "D2", "D3"]
})
df3 = pd.DataFrame({
"E": ["E0", "E1", "E2", "E3"],
"F": ["F0", "F1", "F2", "F3"]
})
# 合并多个DataFrame
result = pd.concat([df1, df2, df3], axis=1)
print(result)
Output:
示例9：使用不同索引的DataFrame进行合并
import pandas as pd
# 创建具有不同索引的DataFrame
df1 = pd.DataFrame({
"A": ["A0", "A1", "A2", "A3"],
"B": ["B0", "B1", "B2", "B3"]
}, index=[0, 1, 2, 3])
df2 = pd.DataFrame({
"C": ["C4", "C5", "C6", "C7"],
"D": ["D4", "D5", "D6", "D7"]
}, index=[4, 5, 6, 7])
# 使用外连接合并DataFrame
result = pd.concat([df1, df2], axis=1, join='outer')
print(result)
Output:
示例10：合并时使用自定义索引名称
import pandas as pd
# 创建DataFrame
df1 = pd.DataFrame({
"A": ["A0", "A1", "A2", "A3"],
"B": ["B0", "B1", "B2", "B3"]
})
df2 = pd.DataFrame({
"C": ["C0", "C1", "C2", "C3"],
"D": ["D0", "D1", "D2", "D3"]
})
# 使用keys参数并指定names
result = pd.concat([df1, df2], axis=1, keys=['Group1', 'Group2'], names=['Upper', 'Lower'])
print(result)
Output:
示例11：合并具有重复列名的DataFrame
import pandas as pd
# 创建具有重复列名的DataFrame
df1 = pd.DataFrame({
"A": ["A0", "A1", "A2", "A3"],
"B": ["B0", "B1", "B2", "B3"]
})
df2 = pd.DataFrame({
"A": ["A4", "A5", "A6", "A7"],
"B": ["B4", "B5", "B6", "B7"]
})
# 合并并处理重复列名
result = pd.concat([df1, df2], axis=1)
print(result)
Output:
示例12：合并时保留原始DataFrame的索引
import pandas as pd
# 创建DataFrame
df1 = pd.DataFrame({
"A": ["A0", "A1", "A2", "A3"],
"B": ["B0", "B1", "B2", "B3"]
}, index=[0, 1, 2, 3])
df2 = pd.DataFrame({
"C": ["C0", "C1", "C2", "C3"],
"D": ["D0", "D1", "D2", "D3"]
}, index=[0, 1, 2, 3])
# 合并并保留原始索引
result = pd.concat([df1, df2], axis=1, ignore_index=False)
print(result)
Output:
示例13：合并时创建新的索引
import pandas as pd
# 创建DataFrame
df1 = pd.DataFrame({
"A": ["A0", "A1", "A2", "A3"],
"B": ["B0", "B1", "B2", "B3"]
})
df2 = pd.DataFrame({
"C": ["C0", "C1", "C2", "C3"],
"D": ["D0", "D1", "D2", "D3"]
})
# 合并并创建新的索引
result = pd.concat([df1, df2], axis=1, ignore_index=True)
print(result)
Output:
示例14：使用多级索引进行复杂合并
import pandas as pd
# 创建DataFrame
df1 = pd.DataFrame({
"A": ["A0", "A1", "A2", "A3"],
"B": ["B0", "B1", "B2", "B3"]
})
df2 = pd.DataFrame({
"C": ["C0", "C1", "C2", "C3"],
"D": ["D0", "D1", "D2", "D3"]
})
# 使用多级索引进行合并
result = pd.concat([df1, df2], axis=1, keys=['Level1', 'Level2'])
print(result)
Output:
示例15：合并时考虑数据排序
import pandas as pd
# 创建DataFrame
df1 = pd.DataFrame({
"A": ["A3", "A2", "A1", "A0"],
"B": ["B3", "B2", "B1", "B0"]
}, index=[3, 2, 1, 0])
df2 = pd.DataFrame({
"C": ["C3", "C2", "C1", "C0"],
"D": ["D3", "D2", "D1", "D0"]
}, index=[3, 2, 1, 0])
# 合并并考虑排序
result = pd.concat([df1, df2], axis=1, sort=True)
print(result)
Output:
以上示例展示了使用Pandas的concat()函数进行列合并的多种情况和技巧。通过这些示例，可以看到concat()函数在数据处理中的灵活性和强大功能。