2

I create a dataset like that,

Gender    response
female    yes
male      yes
female    yes
female    no
male      yes
female    no
male      yes
male      no
female    no

I like to count the yes responses and no responses genderwise. Like there are two females who said No and 2 females who said yes. There are three males said yes and one said no.

I tried to implement this using pandas dataframe.

So far I have tried to write down the query like

df.loc[df['Gender'] == 'female' & (df['response'] == 'yes')]

But I got error. How could I write it down?

Thank you.

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  • The error you are getting is because you missed putting the first condition inside a bracket. Anyways the line you are looking for is df.groupby(['Gender', 'response'], as_index=False).size() Commented Oct 29, 2022 at 6:04

3 Answers 3

Reset to default
2

You can use value_counts with groupby method like this:

df.groupby('Gender')['response'].value_counts()

Response:

Gender  response
female  no          3
        yes         2
male    yes         3
        no          1
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2

Please cross tabulate

pd.crosstab(df['Gender'], df['response']).reset_index()
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1

You can group and count each category like this too:

counts = df.groupby(['Gender','response']).size()

print(counts['female']['yes']) # Show the number of females who responded yes
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