Pandas Convert Columns to Percentages of the Totals

pandas convert columns to percentages of the totals

You can do this using basic pandas operators .div and .sum, using the axis argument to make sure the calculations happen the way you want:

cols = ['<80%', '80-90', '>90']
df[cols] = df[cols].div(df[cols].sum(axis=1), axis=0).multiply(100)

• Calculate the sum of each column (df[cols].sum(axis=1). axis=1 makes the summation occur across the rows, rather than down the columns.
• Divide the dataframe by the resulting series (df[cols].div(df[cols].sum(axis=1), axis=0). axis=0 makes the division happen across the columns.
• To finish, multiply the results by 100 so they are percentages between 0 and 100 instead of proportions between 0 and 1 (or you can skip this step and store them as proportions).

Python Pandas - Convert column to percentage on Groupby DF

Here's one way

In [1371]: (100. * df / df.sum()).round(0)
Out[1371]:
               2014/2015  2015/2016  2016/2017  2017/2018
FinancialYear
April                9.0        6.0        6.0       24.0
May                  7.0        7.0        6.0       29.0
June                 5.0       10.0        6.0       24.0
July                 6.0       10.0        5.0       24.0
August               9.0        8.0       10.0        0.0
September            9.0        6.0       12.0        0.0
October              9.0       10.0        8.0        0.0
November            10.0        8.0        9.0        0.0
December             9.0       11.0        7.0        0.0
January              9.0        8.0       10.0        0.0
February             9.0        7.0       11.0        0.0
March                9.0        8.0       10.0        0.0

And, if you want to rounded to 1 decimal place with value as strings with '%'

In [1375]: (100. * df / df.sum()).round(1).astype(str) + '%'
Out[1375]:
              2014/2015 2015/2016 2016/2017 2017/2018
FinancialYear
April              8.7%      6.4%      6.4%     23.5%
May                7.4%      6.9%      6.1%     29.4%
June               4.8%     10.3%      6.4%     23.5%
July               5.9%     10.3%      5.2%     23.5%
August             9.2%      8.0%      9.9%      0.0%
September          8.9%      6.1%     12.0%      0.0%
October            9.2%      9.8%      7.9%      0.0%
November           9.7%      8.2%      8.7%      0.0%
December           9.2%     10.9%      6.7%      0.0%
January            8.7%      8.0%     10.2%      0.0%
February           9.4%      6.9%     10.8%      0.0%
March              9.2%      8.2%      9.6%      0.0%

Convert Pandas dataframe values to percentage

Use

>>> df.iloc[:, 1:] = df.iloc[:, 1:].div(df['total'], axis=0).mul(100).round(2).astype(str).add(' %')
>>> df 
   ID     col1     col2     col3    total
0   1  28.57 %  42.86 %  28.57 %  100.0 %
1   2  16.67 %  33.33 %   50.0 %  100.0 %
2   3   25.0 %   37.5 %   37.5 %  100.0 %

Pandas - Converting columns in percentage based on first columns value

try via div(),mul() and astype() method:

df[['x%','y%']]=df[['x','y']].div(df['total'],axis=0).mul(100).astype(int)

output of df:

    categorie   total   x       y       x%      y%
0   a           100     10      100     10      100
1   b           1000    100     1000    10      100
2   c           500     5       500     1       100

How to convert a column in a dataframe from decimals to percentages with python

Toy example:

df=DataFrame({
    'No Show (%)':[5e-01, 4e-01]
})
df

Input

No Show (%)
0   0.5
1   0.4

Code

mergedgc.style.format({"No Show (%)": "{:.2%}"})

can be replaced by

df['No Show (%)'] = df['No Show (%)'].transform(lambda x: '{:,.2%}'.format(x))

Output

No Show (%)
0   50.00%
1   40.00%

Edit

Plot

df['No Show (%)'].replace('\%','', regex=True).astype(float).plot()

Pandas transform columns into percentage by group

You can use transform after the groupby and assign the results directly to the column 'record':

gender_mix['record'] = gender_mix\
    .groupby(['user', 'generation'])['record']\
    .transform(lambda x: round((x/sum(x)*100)).astype(int))

Python/Pandas convert pivot table into percentages based on row total

1. You can use the .pipe method combined with .div to perform this column-wise division on all of the columns.
2. You can then use .applymap to apply a string fomratting to each of your values to get the values to appear as percentages (note that they become strings and are no longer mathematically useful)

out = (
    df.pivot_table(
        index='Name', columns='scenario', values='y', 
        aggfunc=np.count_nonzero, margins=True, 
        margins_name='Total', fill_value=0
    )
    .pipe(lambda d: d.div(d['Total'], axis='index'))
    .applymap('{:.0%}'.format)
)

---

example

df = pd.DataFrame({
    'a': [1, 0, 0, 1, 5],
    'b': [20, 20, 10, 50, 15], 
   'c': [50, 20, 50, 100, 20]
})

print(df)
   a   b    c
0  1  20   50
1  0  20   20
2  0  10   50
3  1  50  100
4  5  15   20


out = (
    df.pipe(lambda d: d.div(d['c'], axis='index'))
    .applymap('{:.0%}'.format)
)

print(out)
     a     b     c
0   2%   40%  100%
1   0%  100%  100%
2   0%   20%  100%
3   1%   50%  100%
4  25%   75%  100%

Add column for percentage of total to Pandas dataframe

Option 1

df['DAYSLATE_pct'] = df.DAYSLATE / df.DAYSLATE.sum()

Option 2

Use pd.value_counts instead of groupby

pre_df.DAYSLATE.value_counts(normalize=True)

Calculating percentages for multiple columns

You can groupby 'Page Name' and 'candidato' then find the sum of each of 'Total Interactions','Likes','Comments','Shares','Love','Angry' for each page name and each candidate: totals.

Then use groupby on totals by the first index level (which is "page name") and transform sum function so that you get the sum for each page name transformed for totals and divide totals by it to find the percentages.

Finally join the two DataFrames for the final outcome.

totals = df.groupby(['Page Name','candidato'])[['Total Interactions','Likes','Comments','Shares','Love','Angry']].sum()
percentages = totals.groupby(level=0).transform('sum').rdiv(totals).mul(100).round(2)

out = totals.join(percentages, lsuffix='', rsuffix='_Percentages').reset_index()

This produces a DataFrame that can produce the plot in the question.

Best Way to Calculate Column Percentage of Total Pandas

>>> print df.drop('Total', axis=1).divide(df.Total, axis=0)
   Group1  Group2
0   0.600   0.400
1   0.950   0.050

---

Related Topics