如何使用 Pandas 的 DataFrame 计算百分比

Question

如何用百分比向 Pandas 的 DataFrame 添加另一列？字典的大小可以改变。

>>> 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

如果

10

的百分比确实是您想要的，最简单的方法是稍微调整您的数据摄入量：

>>> 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

对于实际百分比，请改为：

>>> 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

首先，将字典的键设为数据框的索引：

 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']

然后，计算百分比并分配给新列。

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

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

这给你：

         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

import pandas as pd
 
data = {'A': [1, 2, 3, 4, 5], 'B': [10, 20, 30, 40, 50]}
df = pd.DataFrame(data)
# calculate percentage using apply() method and lambda function
 
df['B_Percentage'] = df['B'].apply(lambda x: (x / df['B'].sum()) * 100)
 
print(df)

使用 lambda 可能很有用。可以通过更多的方法来完成。也许这会有所帮助http://www.pythonpandas.com/how-to-calculate-the-percentage-of-a-column-in-pandas/

Answer 4

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

Answer 5

在探索模型训练数据时，我采用以下方法。

import pandas as pd
d = {"set1":[59268, 6166, 115], "set2":[12700, 9892, 238]}
idx_labels = ["Train", "Validation", "Test"]
df = pd.DataFrame(data=d, index=idx_labels)


df
             set1   set2
Train       59268  12700
Validation   6166   9892
Test          115    238


for idx in idx_labels:
    df["sub-totals"] = df.loc[idx].sum()


df    
             set1   set2  sub-totals
Train       59268  12700       88379
Validation   6166   9892       88379
Test          115    238       88379


def compute_ratio(df, target, num_decimal: int = 2) -> pd.Series:
    if target in df.columns:
        divider = df.loc[:, target].sum()
        pct = df.loc[:, target] / divider
    elif target in df.index:
        divider = df.loc[target].sum()
        pct = df.loc[target, :] / divider
    return round(pct, num_decimal)

df["set1_ratio"] = compute_ratio(df, "set1")
df["set2_ratio"] = compute_ratio(df, "set2")
df.loc["totals"] = df.loc[:, :].sum()

df
               set1     set2  sub-totals  set1_ratio  set2_ratio
Train       59268.0  12700.0     88379.0        0.90        0.56
Validation   6166.0   9892.0     88379.0        0.09        0.43
Test          115.0    238.0     88379.0        0.00        0.01
totals      65549.0  22830.0    265137.0        0.99        1.00