如何使用 Pandas 的 DataFrame 计算百分比

Question

How to add another column to Pandas' DataFrame with percentage? The dict can change on size.

>>> import pandas as pd
>>> a = {'Test 1': 4, 'Test 2': 1, 'Test 3': 1, 'Test 4': 9}
>>> p = pd.DataFrame(a.items())
>>> p
        0  1
0  Test 2  1
1  Test 3  1
2  Test 1  4
3  Test 4  9

[4 rows x 2 columns]

Answer 1

If indeed percentage of 10 is what you want, the simplest way is to adjust your intake of the data slightly:

>>> p = pd.DataFrame(a.items(), columns=['item', 'score'])
>>> p['perc'] = p['score']/10
>>> p
Out[370]: 
     item  score  perc
0  Test 2      1   0.1
1  Test 3      1   0.1
2  Test 1      4   0.4
3  Test 4      9   0.9

For real percentages, instead:

>>> p['perc']= p['score']/p['score'].sum()
>>> p
Out[427]: 
     item  score      perc
0  Test 2      1  0.066667
1  Test 3      1  0.066667
2  Test 1      4  0.266667
3  Test 4      9  0.600000

Answer 2

First, make the keys of your dictionary the index of you dataframe:

 import pandas as pd
 a = {'Test 1': 4, 'Test 2': 1, 'Test 3': 1, 'Test 4': 9}
 p = pd.DataFrame([a])
 p = p.T # transform
 p.columns = ['score']

Then, compute the percentage and assign to a new column.

 def compute_percentage(x):
      pct = float(x/p['score'].sum()) * 100
      return round(pct, 2)

 p['percentage'] = p.apply(compute_percentage, axis=1)

This gives you:

         score  percentage
 Test 1      4   26.67
 Test 2      1    6.67
 Test 3      1    6.67
 Test 4      9   60.00

 [4 rows x 2 columns]

Answer 3

df=pd.read_excel("regional cases.xlsx")
df.head()

REGION  CUMILATIVECOUNTS    POPULATION

GREATER         12948       4943075
ASHANTI         4972        5792187
WESTERN         2051        2165241
CENTRAL         1071        2563228



df['Percentage']=round((df['CUMILATIVE COUNTS']/ df['POPULATION']*100)*100,2)
df.head()



REGION  CUMILATIVECOUNTS    POPULATION  Percentage

GREATER 12948               4943075      26.19
ASHANTI 4972                5792187      8.58
WESTERN 2051                2165241      9.47