动态在 Pandas 数据框中添加列
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Add columns in pandas dataframe dynamically
提问by Ishan Bhatt
I have following code to load dataframe
我有以下代码来加载数据框
import pandas as pd
ufo = pd.read_csv('csv_path')
print ufo.loc[[0,1,2] , :]
which gives following output, see the structure of the csv
给出以下输出，请参阅 csv 的结构
City Colors Reported Shape Reported State Time
0 Ithaca NaN TRIANGLE NY 6/1/1930 22:00
1 Willingboro NaN OTHER NJ 6/30/1930 20:00
2 Holyoke NaN OVAL CO 2/15/1931 14:00
Now, I want to add an extra column based on existing column. I have a list which consist of indices of participating columns. It can be 0,1or 0,2,3or 1,2,3anything.
现在，我想根据现有列添加一个额外的列。我有一个由参与列的索引组成的列表。它可以是0,1或0,2,3或1,2,3任何东西。
I need to create it dynamically. I could come up with following
我需要动态创建它。我可以想出以下
df1['combined'] = df1['City']+','+df1['State']
Putting index doesn't seem to work.
I want to join those columns. using ','.join()
放置索引似乎不起作用。我想加入那些专栏。使用','.join()
采纳答案by Psidom
Assuming the data types of all the columns you want to joinare str, you can use []with integer to pick up the columns and use applyto join them:
假设您想要的所有列的数据类型join都是str，您可以使用[]with integer 来获取列并用于apply连接它们：
df[[0,2,3]].apply(','.join, axis=1)
#0 Ithaca,TRIANGLE,NY
#1 Willingboro,OTHER,NJ
#2 Holyoke,OVAL,CO
#dtype: object
回答by Ami Tavory
If the list of indices is l, you can use pd.Series.cat:
如果索引列表是l，则可以使用pd.Series.cat：
df[df.columns[l[0]]].astype(str).str.cat([df[df.columns[i]].astype(str) for i in l[1: ]], sep=',')
Example
例子
In [18]: df = pd.DataFrame({'a': [1, 2], 'b': [2, 'b'], 'c': [3, 'd']})
In [19]: df[df.columns[l[0]]].astype(str).str.cat([df[df.columns[i]].astype(str) for i in l[1: ]], sep=',')
Out[19]:
0 1,2
1 2,b
Name: a, dtype: object
回答by piRSquared
def dyna_join(df, positions):
return pd.concat([df, df.iloc[:, positions].apply(','.join, 1).rename('new_col')], axis=1)
dyna_join(df, [0, -2])
