这篇文章主要介绍了怎么使用Pandas.concat连接DataFrame和Series的相关知识，内容详细易懂，操作简单快捷，具有一定借鉴价值，相信大家阅读完这篇怎么使用Pandas.concat连接DataFrame和Series文章都会有所收获，下面我们一起来看看吧。
pandas.concat的基本用法（）
指定要连接的对象：objs
连接方向的指定（垂直/水平）：axis
指定连接方法（外部连接/内部连接）：join
pandas.DataFrame的连接
pandas.Series的连接
pandas.DataFrame和pandas.Series的连接
使用以下的pandas.DataFrame和pandas.Series为例。
import pandas as pddf1 = pd.DataFrame({'A': ['A1', 'A2', 'A3'], 'B': ['B1', 'B2', 'B3'], 'C': ['C1', 'C2', 'C3']}, index=['ONE', 'TWO', 'THREE'])print(df1)# A B C# ONE A1 B1 C1# TWO A2 B2 C2# THREE A3 B3 C3df2 = pd.DataFrame({'C': ['C2', 'C3', 'C4'], 'D': ['D2', 'D3', 'D4']}, index=['TWO', 'THREE', 'FOUR'])print(df2)# C D# TWO C2 D2# THREE C3 D3# FOUR C4 D4s1 = pd.Series(['X1', 'X2', 'X3'], index=['ONE', 'TWO', 'THREE'], name='X')print(s1)# ONE X1# TWO X2# THREE X3# Name: X, dtype: objects2 = pd.Series(['Y2', 'Y3', 'Y4'], index=['TWO', 'THREE', 'FOUR'], name='Y')print(s2)# TWO Y2# THREE Y3# FOUR Y4# Name: Y, dtype: object
pandas.concat的基本用法（）
指定要连接的对象：objs
通过参数objs指定要连接的pandas.DataFrame和pandas.Series，指定类型为列表或元组。
df_concat = pd.concat([df1, df2])print(df_concat)# A B C D# ONE A1 B1 C1 NaN# TWO A2 B2 C2 NaN# THREE A3 B3 C3 NaN# TWO NaN NaN C2 D2# THREE NaN NaN C3 D3# FOUR NaN NaN C4 D4要连接的对象的数量不限于两个，可以是三个或更多。
df_concat_multi = pd.concat([df1, df2, df1])print(df_concat_multi)# A B C D# ONE A1 B1 C1 NaN# TWO A2 B2 C2 NaN# THREE A3 B3 C3 NaN# TWO NaN NaN C2 D2# THREE NaN NaN C3 D3# FOUR NaN NaN C4 D4# ONE A1 B1 C1 NaN# TWO A2 B2 C2 NaN# THREE A3 B3 C3 NaN结果是创建了一个新的对象，原始对象保持不变。
连接方向的指定（垂直/水平）：axis
垂直或水平方向由axis参数指定。 如果axis = 0，则它们是垂直链接的。默认设置为axis = 0，因此可以省略不写。
df_v = pd.concat([df1, df2], axis=0)print(df_v)# A B C D# ONE A1 B1 C1 NaN# TWO A2 B2 C2 NaN# THREE A3 B3 C3 NaN# TWO NaN NaN C2 D2# THREE NaN NaN C3 D3# FOUR NaN NaN C4 D4axis = 1，水平方向上连接。
df_h = pd.concat([df1, df2], axis=1)print(df_h)# A B C C D# ONE A1 B1 C1 NaN NaN# TWO A2 B2 C2 C2 D2# THREE A3 B3 C3 C3 D3# FOUR NaN NaN NaN C4 D4指定连接方法（外部连接/内部连接）：join
参数join:指定列名（或行名）的并集，或者仅将公共部分保留。
join ='outer’是外部连接。列名（或行名）形成一个联合，保留所有列（或行）。它是默认设置，因此可以省略不写。在这种情况下，原始对象中列（或行）不存在的值将由的缺少值NaN代替。
join ='inner’是内部连接。仅保留具有相同列名（或行名）的列（或行）。
df_v_out = pd.concat([df1, df2], join='outer')print(df_v_out)# A B C D# ONE A1 B1 C1 NaN# TWO A2 B2 C2 NaN# THREE A3 B3 C3 NaN# TWO NaN NaN C2 D2# THREE NaN NaN C3 D3# FOUR NaN NaN C4 D4df_v_in = pd.concat([df1, df2], join='inner')print(df_v_in)# C# ONE C1# TWO C2# THREE C3# TWO C2# THREE C3# FOUR C4水平方向。
df_h_out = pd.concat([df1, df2], axis=1, join='outer')print(df_h_out)# A B C C D# FOUR NaN NaN NaN C4 D4# ONE A1 B1 C1 NaN NaN# THREE A3 B3 C3 C3 D3# TWO A2 B2 C2 C2 D2df_h_in = pd.concat([df1, df2], axis=1, join='inner')print(df_h_in)# A B C C D# TWO A2 B2 C2 C2 D2# THREE A3 B3 C3 C3 D301_Pandas.DataFrame的行名和列名的修改
pandas.DataFrame的连接
将pandas.DataFrames连接在一起时，返回的也是pandas.DataFrame类型的对象。
df_concat = pd.concat([df1, df2])print(df_concat)# A B C D# ONE A1 B1 C1 NaN# TWO A2 B2 C2 NaN# THREE A3 B3 C3 NaN# TWO NaN NaN C2 D2# THREE NaN NaN C3 D3# FOUR NaN NaN C4 D4print(type(df_concat))# <class 'pandas.core.frame.DataFrame'>pandas.Series的连接
如果是pandas.Series之间的连接，则垂直连接（默认值axis= 0）返回的也是pandas.Series类型的对象。
s_v = pd.concat([s1, s2])print(s_v)# ONE X1# TWO X2# THREE X3# TWO Y2# THREE Y3# FOUR Y4# dtype: objectprint(type(s_v))# <class 'pandas.core.series.Series'>axis = 1时，水平方向连接，返回pandas.DataFrame类型的对象。
s_h = pd.concat([s1, s2], axis=1)print(s_h)# X Y# FOUR NaN Y4# ONE X1 NaN# THREE X3 Y3# TWO X2 Y2print(type(s_h))# <class 'pandas.core.frame.DataFrame'>也可以使用参数join。
s_h_in = pd.concat([s1, s2], axis=1, join='inner')print(s_h_in)# X Y# TWO X2 Y2# THREE X3 Y3pandas.DataFrame和pandas.Series的连接
对于pandas.DataFrame和pandas.Series连接，水平连接（axis= 1）将pandas.Series添加为新列。列名称是pandas.Series的名称。
df_s_h = pd.concat([df1, s2], axis=1)print(df_s_h)# A B C Y# FOUR NaN NaN NaN Y4# ONE A1 B1 C1 NaN# THREE A3 B3 C3 Y3# TWO A2 B2 C2 Y2也可以使用参数join。
df_s_h_in = pd.concat([df1, s2], axis=1, join='inner')print(df_s_h_in)# A B C Y# TWO A2 B2 C2 Y2# THREE A3 B3 C3 Y3垂直连接（axis = 0）。
df_s_v = pd.concat([df1, s2])print(df_s_v)# A B C 0# ONE A1 B1 C1 NaN# TWO A2 B2 C2 NaN# THREE A3 B3 C3 NaN# TWO NaN NaN NaN Y2# THREE NaN NaN NaN Y3# FOUR NaN NaN NaN Y4添加行，可以在.loc中指定新的行名称并分配值，或使用append（）方法。
df1.loc['FOUR'] = ['A4', 'B4', 'C4']print(df1)# A B C# ONE A1 B1 C1# TWO A2 B2 C2# THREE A3 B3 C3# FOUR A4 B4 C4s = pd.Series(['A5', 'B5', 'C5'], index=df1.columns, name='FIVE')print(s)# A A5# B B5# C C5# Name: FIVE, dtype: objectdf_append = df1.append(s)print(df_append)# A B C# ONE A1 B1 C1# TWO A2 B2 C2# THREE A3 B3 C3# FOUR A4 B4 C4# FIVE A5 B5 C5关于“怎么使用Pandas.concat连接DataFrame和Series”这篇文章的内容就介绍到这里，感谢各位的阅读！相信大家对“怎么使用Pandas.concat连接DataFrame和Series”知识都有一定的了解，大家如果还想学习更多知识，欢迎关注编程网行业资讯频道。
