Question

我有2个具有相同结构的输入数据框（df1和df2），我想创建第3个输入数据框（output_df），并具有输入数据框的所有行组合

df1 = pd.DataFrame([["John","18","a"],["Jane","19","b"],["Jim","20","c"]],columns=['Name','Age','Function'])
df2 = pd.DataFrame([["Don","21","d"],["Diana","22","e"],["Dave","23","f"]],columns=['Name','Age','Function'])

output_df=pd.DataFrame([["John_Don","18_21","a_d"],
["John_Diana","18_22","a_e"],
["John_Dave","18_23","a_f"],
["Jane_Don","19_21","b_d"],
["Jane_Diana","19_22","b_e"],
["Jane_Dave","19_23","b_f"],
["Jim_Don","20_21","c_d"],
["Jim_Diana","20_22","c_e"],
["Jim_Dave","20_23","c_f"]],columns=['Name','Age','Function'])

新数据框将具有初始数据框相应列的总和（“ +”）。（我知道字符串是串联的-如果输入是字符串，这就是我要追求的目标）

以下代码创建了output_df，但它为空，并且代码花费的时间太长了。下面的示例代码仅作为2x10记录运行。最终，我将处理来自每个数据框的数千条记录。

Q1：填充输出数据框时我缺少什么？

Q2：如何提高代码效率？

output_df=pandas.DataFrame(columns=['Name','Age','Function'])
i=0
for lendf1 in range (10):
    for lendf2 in range(10):
        output_df=output_df.append(pandas.Series(),ignore_index=True)
        i=i+1
        for column in output_df:
            output_df[column][i]=df1[column][lendf1:lendf1+1]+df2[column][lendf2:lendf2+1]

Answer 1

我相信您正在寻找这个：

first = pd.Series(['a', 'b', 'c', 'd', 'e'])
second = pd.Series(['f', 'g', 'h', 'i', 'j'])
pd.DataFrame(np.add.outer(first, second))

输出：

    0   1   2   3   4
0  af  ag  ah  ai  aj
1  bf  bg  bh  bi  bj
2  cf  cg  ch  ci  cj
3  df  dg  dh  di  dj
4  ef  eg  eh  ei  ej

请注意，输入应为pd.Series类型，而不是数据帧。

Answer 2

我认为您正在尝试同时连接数据框的两个列。请尝试以下代码为您工作。

import pandas as pd

df1 = pd.DataFrame([["John","18","a"],["Jane","19","b"],["Jim","20","c"]],columns=['Name','Age','Function'])
df2 = pd.DataFrame([["Don","21","d"],["Diana","22","e"],["Dave","23","f"]],columns=['Name','Age','Function'])

cols = list(df1)

out_list = []
for ind1, row1 in df1.iterrows():
    for ind2, row2 in df2.iterrows():
        in_list = []
        for i in range(0, len(cols)):
            in_list.append(row1[cols[i]] + '_' + row2[cols[i]])
        out_list.append(in_list)

outdf = pd.DataFrame(out_list, columns=cols)
print outdf

Python Pandas从现有数据框的所有行组合创建一个新的数据框

2 个答案: