If I have a the following dataframe:
df = pd.DataFrame({'name':['john','mary','peter','jeff','bill','lisa','jose'], 'gender':['M','F','M','M','M','F','M'],'state':['california','dc','california','dc','california','texas','texas'],'num_children':[2,0,0,3,2,1,4],'num_pets':[5,1,0,5,2,2,3]})
name gender state num_children num_pets
0 john M california 2 5
1 mary F dc 0 1
2 peter M california 0 0
3 jeff M dc 3 5
4 bill M california 2 2
5 lisa F texas 1 2
6 jose M texas 4 3
I want to create a new row and column pct.
to get the percentage of zero values in columns num_children
and num_pets
Expected output:
name gender state num_children num_pets pct.
0 pct. 28.6% 14.3%
1 john M california 2 5 0%
2 mary F dc 0 1 50%
3 peter M california 0 0 100%
4 jeff M dc 3 5 0%
5 bill M california 2 2 0%
6 lisa F texas 1 2 0%
7 jose M texas 4 3 0%
I have calculated percentage of zero in each row for targets columns:
df['pct'] = df[['num_children', 'num_pets']].astype(bool).sum(axis=1)/2
df['pct.'] = 1-df['pct']
del df['pct']
df['pct.'] = pd.Series(["{0:.0f}%".format(val * 100) for val in df['pct.']], index = df.index)
name gender state num_children num_pets pct.
0 john M california 2 5 0%
1 mary F dc 0 1 50%
2 peter M california 0 0 100%
3 jeff M dc 3 5 0%
4 bill M california 2 2 0%
5 lisa F texas 1 2 0%
6 jose M texas 4 3 0%
But i don't know how to insert results below to row of pct
. as expected output, please help me to get expected result in more pythonic way. Thanks.
df[['num_children', 'num_pets']].astype(bool).sum(axis=0)/len(df.num_children)
Out[153]:
num_children 0.714286
num_pets 0.857143
dtype: float64
UPDATE: same thing but for calculation of sums, great thanks to @jezrael:
df['sums'] = df[['num_children', 'num_pets']].sum(axis=1)
df1 = (df[['num_children', 'num_pets']].sum()
.to_frame()
.T
.assign(name='sums'))
df = pd.concat([df1.reindex(columns=df.columns, fill_value=''), df],
ignore_index=True, sort=False)
print (df)
name gender state num_children num_pets sums
0 sums 12 18
1 john M california 2 5 7
2 mary F dc 0 1 1
3 peter M california 0 0 0
4 jeff M dc 3 5 8
5 bill M california 2 2 4
6 lisa F texas 1 2 3
7 jose M texas 4 3 7
You can use mean
with boolean mask by compare 0
values by DataFrame.eq
, because sum/len=mean
by definition, multiple by 100
and add percentage with apply
:
s = df[['num_children', 'num_pets']].eq(0).mean(axis=1)
df['pct'] = s.mul(100).apply("{0:.0f}%".format)
For first row create new DataFrame
with same columns like original and concat
together:
df1 = (df[['num_children', 'num_pets']].eq(0)
.mean()
.mul(100)
.apply("{0:.1f}%".format)
.to_frame()
.T
.assign(name='pct.'))
df = pd.concat([df1.reindex(columns=df.columns, fill_value=''), df],
ignore_index=True, sort=False)
print (df)
name gender state num_children num_pets pct
0 pct. 28.6% 14.3%
1 john M california 2 5 0%
2 mary F dc 0 1 50%
3 peter M california 0 0 100%
4 jeff M dc 3 5 0%
5 bill M california 2 2 0%
6 lisa F texas 1 2 0%
7 jose M texas 4 3 0%
The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.