[英]pandas value_counts include all values before groupby
假設我有以下數據框:
df = pd.DataFrame([['a',1, -1], ['a', 1, -1], ['b', 0, -1], ['c', -1, -1]] ,columns = ['col1', 'col2', 'col3'])
df
col1 col2 col3
0 a 1 -1
1 a 1 -1
2 b 0 -1
3 c -1 -1
現在我想按列對 df 進行分組,並且對於每個列,分別計算列col1
值的出現次數。
groupby_df = df.groupby('col1')
for a,b in groupby_df:
print("{0} -> \n{1}".format(a, b['col1'].value_counts().sort_index()))
我得到:
a ->
a 2
Name: col1, dtype: int64
b ->
b 1
Name: col1, dtype: int64
c ->
c 1
Name: col1, dtype: int64
但是我想單獨統計出現次數,仍然包括所有的列值,如下:
a ->
a 2
b 0
c 0
Name: col1, dtype: int64
b ->
a 0
b 1
c 0
Name: col1, dtype: int64
c ->
a 0
b 0
c 1
Name: col1, dtype: int64
任何幫助將不勝感激!
嘗試使用.reindex() :
import pandas as pd
df = pd.DataFrame([['a',1, -1], ['a', 1, -1], ['b', 0, -1], ['c', -1, -1]] ,columns = ['col1', 'col2', 'col3'])
# Create index using unique values of col1.
uniques = pd.Index(df['col1'].unique())
# Group.
groupby_df = df.groupby('col1')
# Use reindex to assign and autoamtically align the value counts with the index.
for a, b in groupby_df:
print(b['col1'].value_counts().sort_index().reindex(uniques, fill_value = 0))
給出:
a 2
b 0
c 0
Name: col1, dtype: int64
a 0
b 1
c 0
Name: col1, dtype: int64
a 0
b 0
c 1
Name: col1, dtype: int64
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