输入:
Date letters numbers mixed new
0 1/2/2014 a 6 z1 1/2/2014 a
1 1/2/2014 a 3 z1 1/2/2014 a
2 1/3/2014 c 1 x3 1/3/2014 c
我想分组new
和总和numbers
,以便输出为:
Date letters numbers mixed new
0 1/2/2014 a 9 z1 1/2/2014 a
1 1/3/2014 c 1 x3 1/3/2014 c
我在这里看过:http://pandas.pydata.org/pandas-docs/stable/groupby.html但没有运气。
这是我的代码:
import pandas
a=[['Date', 'letters', 'numbers', 'mixed'], ['1/2/2014', 'a', '6', 'z1'], ['1/2/2014', 'a', '3', 'z1'], ['1/3/2014', 'c', '1', 'x3']]
df = pandas.DataFrame.from_records(a[1:],columns=a[0])
f=[]
for i in range(0,len(df)):
f.append(df['Date'][i] + ' ' + df['letters'][i])
df['new']=f
此外,任何在没有循环的情况下连接date
和letters
的技巧也会有所帮助。
答案 0 :(得分:1)
您必须将numbers
列转换为int
import pandas as pd
a=[['Date', 'letters', 'numbers', 'mixed'], ['1/2/2014', 'a', '6', 'z1'], ['1/2/2014', 'a', '3', 'z1'], ['1/3/2014', 'c', '1', 'x3']]
df = pd.DataFrame.from_records(a[1:],columns=a[0])
df['new'] = df.Date + " " + df.letters
df.numbers = df.numbers.astype(int)
print df
Date letters numbers mixed new
0 1/2/2014 a 6 z1 1/2/2014 a
1 1/2/2014 a 3 z1 1/2/2014 a
2 1/3/2014 c 1 x3 1/3/2014 c
您可以获取要合并的数据框:
df_to_merge = df[df.columns[~df.columns.isin(['numbers'])]].drop_duplicates()
然后你可以做groupby
df_grouped = pd.DataFrame(df.groupby('new').numbers.sum()).reset_index()
要获得您发布的结果merge
df_result = df_to_merge.merge(df_grouped)
print df_result
Date letters mixed new numbers
0 1/2/2014 a z1 1/2/2014 a 9
1 1/3/2014 c x3 1/3/2014 c 1