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簡単なピボット
まずpivot使用してみてください：
import pandas as pd
import numpy as np
df = pd.DataFrame({'Name':['Mary', 'Josh','Jon','Lucy', 'Jane', 'Sue'],
'Age':[34, 37, 29, 40, 29, 31],
'City':['Boston','New York', 'Chicago', 'Los Angeles', 'Chicago', 'Boston'],
'Position':['Manager','Programmer','Manager','Manager','Programmer', 'Programmer']},
columns=['Name','Position','City','Age'])
print (df)
Name Position City Age
0 Mary Manager Boston 34
1 Josh Programmer New York 37
2 Jon Manager Chicago 29
3 Lucy Manager Los Angeles 40
4 Jane Programmer Chicago 29
5 Sue Programmer Boston 31
print (df.pivot(index='Position', columns='City', values='Age'))
City Boston Chicago Los Angeles New York
Position
Manager 34.0 29.0 40.0 NaN
Programmer 31.0 29.0 NaN 37.0
インデックスをリセットする必要がある場合は、列名を削除してNaN値を入力します。
#pivoting by numbers - column Age
print (df.pivot(index='Position', columns='City', values='Age')
.reset_index()
.rename_axis(None, axis=1)
.fillna(0))
Position Boston Chicago Los Angeles New York
0 Manager 34.0 29.0 40.0 0.0
1 Programmer 31.0 29.0 0.0 37.0
#pivoting by strings - column Name
print (df.pivot(index='Position', columns='City', values='Name'))
City Boston Chicago Los Angeles New York
Position
Manager Mary Jon Lucy None
Programmer Sue Jane None Josh
集約によるピボット
import pandas as pd
import numpy as np
df = pd.DataFrame({'Name':['Mary', 'Jon','Lucy', 'Jane', 'Sue', 'Mary', 'Lucy'],
'Age':[35, 37, 40, 29, 31, 26, 28],
'City':['Boston', 'Chicago', 'Los Angeles', 'Chicago', 'Boston', 'Boston', 'Chicago'],
'Position':['Manager','Manager','Manager','Programmer', 'Programmer','Manager','Manager'],
'Sex':['Female','Male','Female','Female', 'Female','Female','Female']},
columns=['Name','Position','City','Age','Sex'])
print (df)
Name Position City Age Sex
0 Mary Manager Boston 35 Female
1 Jon Manager Chicago 37 Male
2 Lucy Manager Los Angeles 40 Female
3 Jane Programmer Chicago 29 Female
4 Sue Programmer Boston 31 Female
5 Mary Manager Boston 26 Female
6 Lucy Manager Chicago 28 Female
pivot使用する場合、エラーが発生します：
print (df.pivot(index='Position', columns='City', values='Age'))
ValueError：インデックスに重複したエントリが含まれています。
集約関数でpivot_tableを使用する：
#default aggfunc is np.mean
print (df.pivot_table(index='Position', columns='City', values='Age'))
City Boston Chicago Los Angeles
Position
Manager 30.5 32.5 40.0
Programmer 31.0 29.0 NaN
print (df.pivot_table(index='Position', columns='City', values='Age', aggfunc=np.mean))
City Boston Chicago Los Angeles
Position
Manager 30.5 32.5 40.0
Programmer 31.0 29.0 NaN
もう1つのagg機能：
print (df.pivot_table(index='Position', columns='City', values='Age', aggfunc=sum))
City Boston Chicago Los Angeles
Position
Manager 61.0 65.0 40.0
Programmer 31.0 29.0 NaN
#lost data !!!
print (df.pivot_table(index='Position', columns='City', values='Age', aggfunc='first'))
City Boston Chicago Los Angeles
Position
Manager 35.0 37.0 40.0
Programmer 31.0 29.0 NaN
string値を持つstring集約する必要がある場合：
print (df.pivot_table(index='Position', columns='City', values='Name'))
DataError：集計する数値型がありません
これらの機能を使用することができます：
print (df.pivot_table(index='Position', columns='City', values='Name', aggfunc='first'))
City Boston Chicago Los Angeles
Position
Manager Mary Jon Lucy
Programmer Sue Jane None
print (df.pivot_table(index='Position', columns='City', values='Name', aggfunc='last'))
City Boston Chicago Los Angeles
Position
Manager Mary Lucy Lucy
Programmer Sue Jane None
print (df.pivot_table(index='Position', columns='City', values='Name', aggfunc='sum'))
City Boston Chicago Los Angeles
Position
Manager MaryMary JonLucy Lucy
Programmer Sue Jane None
print (df.pivot_table(index='Position', columns='City', values='Name', aggfunc=', '.join))
City Boston Chicago Los Angeles
Position
Manager Mary, Mary Jon, Lucy Lucy
Programmer Sue Jane None
print (df.pivot_table(index='Position', columns='City', values='Name', aggfunc=', '.join, fill_value='-')
.reset_index()
.rename_axis(None, axis=1))
Position Boston Chicago Los Angeles
0 Manager Mary, Mary Jon, Lucy Lucy
1 Programmer Sue Jane -
セックスに関する情報はまだ使用されていません。列の1つで切り替えることも、別のレベルとして追加することもできます。
print (df.pivot_table(index='Position', columns=['City','Sex'], values='Age', aggfunc='first'))
City Boston Chicago Los Angeles
Sex Female Female Male Female
Position
Manager 35.0 28.0 37.0 40.0
Programmer 31.0 29.0 NaN NaN
インデックス、列、および値のいずれの属性でも複数の列を指定できます。
print (df.pivot_table(index=['Position','Sex'], columns='City', values='Age', aggfunc='first'))
City Boston Chicago Los Angeles
Position Sex
Manager Female 35.0 28.0 40.0
Male NaN 37.0 NaN
Programmer Female 31.0 29.0 NaN
いくつかの集約関数の適用
1回のピボットで複数の機能を簡単に適用できます。
In [23]: import numpy as np
In [24]: df.pivot_table(index='Position', values='Age', aggfunc=[np.mean, np.std])
Out[24]:
mean std
Position
Manager 34.333333 5.507571
Programmer 32.333333 4.163332
場合によっては、特定の機能を特定の列に適用することができます。
In [35]: df['Random'] = np.random.random(6)
In [36]: df
Out[36]:
Name Position City Age Random
0 Mary Manager Boston 34 0.678577
1 Josh Programmer New York 37 0.973168
2 Jon Manager Chicago 29 0.146668
3 Lucy Manager Los Angeles 40 0.150120
4 Jane Programmer Chicago 29 0.112769
5 Sue Programmer Boston 31 0.185198
For example, find the mean age, and standard deviation of random by Position:
In [37]: df.pivot_table(index='Position', aggfunc={'Age': np.mean, 'Random': np.std})
Out[37]:
Age Random
Position
Manager 34.333333 0.306106
Programmer 32.333333 0.477219
個々の列にも適用される関数のリストを渡すことができます。
In [38]: df.pivot_table(index='Position', aggfunc={'Age': np.mean, 'Random': [np.mean, np.std]})]
Out[38]:
Age Random
mean mean std
Position
Manager 34.333333 0.325122 0.306106
Programmer 32.333333 0.423712 0.477219
積み重ねと積み重ね
import pandas as pd
import numpy as np
np.random.seed(0)
tuples = list(zip(*[['bar', 'bar', 'foo', 'foo', 'qux', 'qux'],
['one', 'two', 'one', 'two','one', 'two']]))
idx = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
df = pd.DataFrame(np.random.randn(6, 2), index=idx, columns=['A', 'B'])
print (df)
A B
first second
bar one 1.764052 0.400157
two 0.978738 2.240893
foo one 1.867558 -0.977278
two 0.950088 -0.151357
qux one -0.103219 0.410599
two 0.144044 1.454274
print (df.stack())
first second
bar one A 1.764052
B 0.400157
two A 0.978738
B 2.240893
foo one A 1.867558
B -0.977278
two A 0.950088
B -0.151357
qux one A -0.103219
B 0.410599
two A 0.144044
B 1.454274
dtype: float64
#reset index, rename column name
print (df.stack().reset_index(name='val2').rename(columns={'level_2': 'val1'}))
first second val1 val2
0 bar one A 1.764052
1 bar one B 0.400157
2 bar two A 0.978738
3 bar two B 2.240893
4 foo one A 1.867558
5 foo one B -0.977278
6 foo two A 0.950088
7 foo two B -0.151357
8 qux one A -0.103219
9 qux one B 0.410599
10 qux two A 0.144044
11 qux two B 1.454274
print (df.unstack())
A B
second one two one two
first
bar 1.764052 0.978738 0.400157 2.240893
foo 1.867558 0.950088 -0.977278 -0.151357
qux -0.103219 0.144044 0.410599 1.454274
rename_axis （ pandas新0.18.0 ）：
#reset index, remove columns names
df1 = df.unstack().reset_index().rename_axis((None,None), axis=1)
#reset MultiIndex in columns with list comprehension
df1.columns = ['_'.join(col).strip('_') for col in df1.columns]
print (df1)
first A_one A_two B_one B_two
0 bar 1.764052 0.978738 0.400157 2.240893
1 foo 1.867558 0.950088 -0.977278 -0.151357
2 qux -0.103219 0.144044 0.410599 1.454274
パンダ0.18.0
#reset index
df1 = df.unstack().reset_index()
#remove columns names
df1.columns.names = (None, None)
#reset MultiIndex in columns with list comprehension
df1.columns = ['_'.join(col).strip('_') for col in df1.columns]
print (df1)
first A_one A_two B_one B_two
0 bar 1.764052 0.978738 0.400157 2.240893
1 foo 1.867558 0.950088 -0.977278 -0.151357
2 qux -0.103219 0.144044 0.410599 1.454274
クロス集計
import pandas as pd
df = pd.DataFrame({'Sex': ['M', 'M', 'F', 'M', 'F', 'F', 'M', 'M', 'F', 'F'],
'Age': [20, 19, 17, 35, 22, 22, 12, 15, 17, 22],
'Heart Disease': ['Y', 'N', 'Y', 'N', 'N', 'Y', 'N', 'Y', 'N', 'Y']})
df
Age Heart Disease Sex
0 20 Y M
1 19 N M
2 17 Y F
3 35 N M
4 22 N F
5 22 Y F
6 12 N M
7 15 Y M
8 17 N F
9 22 Y F
pd.crosstab(df['Sex'], df['Heart Disease'])
Hearth Disease N Y
Sex
F 2 3
M 3 2
ドット表記の使用：
pd.crosstab(df.Sex, df.Age)
Age 12 15 17 19 20 22 35
Sex
F 0 0 2 0 0 3 0
M 1 1 0 1 1 0 1
DFの転置を取得する：
pd.crosstab(df.Sex, df.Age).T
Sex F M
Age
12 0 1
15 0 1
17 2 0
19 0 1
20 0 1
22 3 0
35 0 1
マージンや累積を取得する：
pd.crosstab(df['Sex'], df['Heart Disease'], margins=True)
Heart Disease N Y All
Sex
F 2 3 5
M 3 2 5
All 5 5 10
累積の転置を取得する：
pd.crosstab(df['Sex'], df['Age'], margins=True).T
Sex F M All
Age
12 0 1 1
15 0 1 1
17 2 0 2
19 0 1 1
20 0 1 1
22 3 0 3
35 0 1 1
All 5 5 10
パーセンテージを得る：
pd.crosstab(df["Sex"],df['Heart Disease']).apply(lambda r: r/len(df), axis=1)
Heart Disease N Y
Sex
F 0.2 0.3
M 0.3 0.2
累積して100倍する：
df2 = pd.crosstab(df["Age"],df['Sex'], margins=True ).apply(lambda r: r/len(df)*100, axis=1)
df2
Sex F M All
Age
12 0.0 10.0 10.0
15 0.0 10.0 10.0
17 20.0 0.0 20.0
19 0.0 10.0 10.0
20 0.0 10.0 10.0
22 30.0 0.0 30.0
35 0.0 10.0 10.0
All 50.0 50.0 100.0
DFから列を削除する（片方向）：
df2[["F","M"]]
Sex F M
Age
12 0.0 10.0
15 0.0 10.0
17 20.0 0.0
19 0.0 10.0
20 0.0 10.0
22 30.0 0.0
35 0.0 10.0
All 50.0 50.0
パンダは広いから長いへ行くために溶ける
>>> df
ID Year Jan_salary Feb_salary Mar_salary
0 1 2016 4500 4200 4700
1 2 2016 3800 3600 4400
2 3 2016 5500 5200 5300
>>> melted_df = pd.melt(df,id_vars=['ID','Year'],
value_vars=['Jan_salary','Feb_salary','Mar_salary'],
var_name='month',value_name='salary')
>>> melted_df
ID Year month salary
0 1 2016 Jan_salary 4500
1 2 2016 Jan_salary 3800
2 3 2016 Jan_salary 5500
3 1 2016 Feb_salary 4200
4 2 2016 Feb_salary 3600
5 3 2016 Feb_salary 5200
6 1 2016 Mar_salary 4700
7 2 2016 Mar_salary 4400
8 3 2016 Mar_salary 5300
>>> melted_['month'] = melted_['month'].str.replace('_salary','')
>>> import calendar
>>> def mapper(month_abbr):
... # from http://stackoverflow.com/a/3418092/42346
... d = {v: str(k).zfill(2) for k,v in enumerate(calendar.month_abbr)}
... return d[month_abbr]
>>> melted_df['month'] = melted_df['month'].apply(mapper)
>>> melted_df
ID Year month salary
0 1 2016 01 4500
1 2 2016 01 3800
2 3 2016 01 5500
3 1 2016 02 4200
4 2 2016 02 3600
5 3 2016 02 5200
6 1 2016 03 4700
7 2 2016 03 4400
8 3 2016 03 5300
列内のCSV文字列を複数の行に分割（再形成）
import pandas as pd
df = pd.DataFrame([{'var1': 'a,b,c', 'var2': 1, 'var3': 'XX'},
{'var1': 'd,e,f,x,y', 'var2': 2, 'var3': 'ZZ'}])
print(df)
reshaped = \
(df.set_index(df.columns.drop('var1',1).tolist())
.var1.str.split(',', expand=True)
.stack()
.reset_index()
.rename(columns={0:'var1'})
.loc[:, df.columns]
)
print(reshaped)
出力：
var1 var2 var3
0 a,b,c 1 XX
1 d,e,f,x,y 2 ZZ
var1 var2 var3
0 a 1 XX
1 b 1 XX
2 c 1 XX
3 d 2 ZZ
4 e 2 ZZ
5 f 2 ZZ
6 x 2 ZZ
7 y 2 ZZ
Modified text is an extract of the original Stack Overflow Documentation
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