CODEWITHSUNDEEP
pythonpandasdataframe
Vibhu
Vibhu

Reputation: 361

How to group rows so as to use value_counts on the created groups with pandas?

I have some customer data such as this in a data frame:

S No Country Sex
1    Spain   M
2    Norway  F
3    Mexico  M
...

I want to have an output such as this:

Spain
M = 1207
F = 230

Norway 
M = 33
F = 102

...

I have a basic notion that I want to group my rows based on their countries with something like df.groupby(df.Country), and on the selected rows, I need to run something like df.Sex.value_counts()

Thanks!

Upvotes: 1

Views: 182

Answers (2)

jezrael
jezrael

Reputation: 862611

I think need crosstab:

df = pd.crosstab(df.Sex, df.Country)

Or if want use your solution add unstack for columns with first level of MultiIndex:

df = df.groupby(df.Country).Sex.value_counts().unstack(level=0, fill_value=0)
print (df)
Country  Mexico  Norway  Spain
Sex                           
F             0       1      0
M             1       0      1

EDIT:

If want add more columns then is possible set which level parameter is converted to columns:

df1 = df.groupby([df.No, df.Country]).Sex.value_counts().unstack(level=0, fill_value=0).reset_index()
print (df1)
No Country Sex  1  2  3
0   Mexico   M  0  0  1
1   Norway   F  0  1  0
2    Spain   M  1  0  0

df2 = df.groupby([df.No, df.Country]).Sex.value_counts().unstack(level=1, fill_value=0).reset_index()
print (df2)
Country  No Sex  Mexico  Norway  Spain
0         1   M       0       0      1
1         2   F       0       1      0
2         3   M       1       0      0

df2 = df.groupby([df.No, df.Country]).Sex.value_counts().unstack(level=2, fill_value=0).reset_index()
print (df2)
Sex  No Country  F  M
0     1   Spain  0  1
1     2  Norway  1  0
2     3  Mexico  0  1

Upvotes: 2

jpp
jpp

Reputation: 164663

You can also use pandas.pivot_table:

res = df.pivot_table(index='Country', columns='Sex', aggfunc='count', fill_value=0)

print(res)

        SNo   
Sex       F  M
Country       
Mexico    0  1
Norway    1  0
Spain     0  1

Upvotes: 1

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