import pandas as pd
df = pd.read_csv('https://query.data.world/s/Hfu_PsEuD1Z_yJHmGaxWTxvkz7W_b0')
percent= 100*(len(df.loc[:,df.isnull().sum(axis=0)>=1 ].index) / len(df.index))
print(round(percent,2))
输入为https://query.data.world/s/Hfu_PsEuD1Z_yJHmGaxWTxvkz7W_b0
，输出应该是
Ord_id 0.00
Prod_id 0.00
Ship_id 0.00
Cust_id 0.00
Sales 0.24
Discount 0.65
Order_Quantity 0.65
Profit 0.65
Shipping_Cost 0.65
Product_Base_Margin 1.30
dtype: float64
答案 0 :(得分：11)
这个怎么样？我想我实际上曾经在这里找到过类似的东西，但是现在看不到...
percent_missing = df.isnull().sum() * 100 / len(df)
missing_value_df = pd.DataFrame({'column_name': df.columns,
'percent_missing': percent_missing})
如果要对丢失的百分比进行排序，请按照上述步骤操作：
missing_value_df.sort_values('percent_missing', inplace=True)
如评论中所述，您也许也可以仅通过上面我的代码中的第一行即可：
percent_missing = df.isnull().sum() * 100 / len(df)
答案 1 :(得分：9)
我们将mean与isnull一起使用：
df.isnull().mean() * 100
输出：
Ord_id 0.000000
Prod_id 0.000000
Ship_id 0.000000
Cust_id 0.000000
Sales 0.238124
Discount 0.654840
Order_Quantity 0.654840
Profit 0.654840
Shipping_Cost 0.654840
Product_Base_Margin 1.297774
dtype: float64
IIUC：
df.isnull().sum() / df.shape[0] * 100.00
输出：
Ord_id 0.000000
Prod_id 0.000000
Ship_id 0.000000
Cust_id 0.000000
Sales 0.238124
Discount 0.654840
Order_Quantity 0.654840
Profit 0.654840
Shipping_Cost 0.654840
Product_Base_Margin 1.297774
dtype: float64
答案 2 :(得分：4)
涵盖所有 missing 值并四舍五入结果：
((df.isnull() | df.isna()).sum() * 100 / df.index.size).round(2)
输出：
Out[556]:
Ord_id 0.00
Prod_id 0.00
Ship_id 0.00
Cust_id 0.00
Sales 0.24
Discount 0.65
Order_Quantity 0.65
Profit 0.65
Shipping_Cost 0.65
Product_Base_Margin 1.30
dtype: float64
答案 3 :(得分：4)
单线解决方案
df.isnull().mean().round(4).mul(100).sort_values(ascending=False)
答案 4 :(得分：0)
import numpy as np
import pandas as pd
raw_data = {'first_name': ['Jason', np.nan, 'Tina', 'Jake', 'Amy'],
'last_name': ['Miller', np.nan, np.nan, 'Milner', 'Cooze'],
'age': [22, np.nan, 23, 24, 25],
'sex': ['m', np.nan, 'f', 'm', 'f'],
'Test1_Score': [4, np.nan, 0, 0, 0],
'Test2_Score': [25, np.nan, np.nan, 0, 0]}
results = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'sex', 'Test1_Score', 'Test2_Score'])
results
first_name last_name age sex Test1_Score Test2_Score
0 Jason Miller 22.0 m 4.0 25.0
1 NaN NaN NaN NaN NaN NaN
2 Tina NaN 23.0 f 0.0 NaN
3 Jake Milner 24.0 m 0.0 0.0
4 Amy Cooze 25.0 f 0.0 0.0
您可以使用以下功能，该功能将在Dataframe中提供输出
只需复制并粘贴以下函数，然后通过传递您的pandas Dataframe来调用它
def missing_zero_values_table(df):
zero_val = (df == 0.00).astype(int).sum(axis=0)
mis_val = df.isnull().sum()
mis_val_percent = 100 * df.isnull().sum() / len(df)
mz_table = pd.concat([zero_val, mis_val, mis_val_percent], axis=1)
mz_table = mz_table.rename(
columns = {0 : 'Zero Values', 1 : 'Missing Values', 2 : '% of Total Values'})
mz_table['Total Zero Missing Values'] = mz_table['Zero Values'] + mz_table['Missing Values']
mz_table['% Total Zero Missing Values'] = 100 * mz_table['Total Zero Missing Values'] / len(df)
mz_table['Data Type'] = df.dtypes
mz_table = mz_table[
mz_table.iloc[:,1] != 0].sort_values(
'% of Total Values', ascending=False).round(1)
print ("Your selected dataframe has " + str(df.shape[1]) + " columns and " + str(df.shape[0]) + " Rows.\n"
"There are " + str(mz_table.shape[0]) +
" columns that have missing values.")
# mz_table.to_excel('D:/sampledata/missing_and_zero_values.xlsx', freeze_panes=(1,0), index = False)
return mz_table
missing_zero_values_table(results)
输出
Your selected dataframe has 6 columns and 5 Rows.
There are 6 columns that have missing values.
Zero Values Missing Values % of Total Values Total Zero Missing Values % Total Zero Missing Values Data Type
last_name 0 2 40.0 2 40.0 object
Test2_Score 2 2 40.0 4 80.0 float64
first_name 0 1 20.0 1 20.0 object
age 0 1 20.0 1 20.0 float64
sex 0 1 20.0 1 20.0 object
Test1_Score 3 1 20.0 4 80.0 float64
如果要保持简单，则可以使用以下函数来获取％中的缺失值
def missing(dff):
print (round((dff.isnull().sum() * 100/ len(dff)),2).sort_values(ascending=False))
missing(results)
Test2_Score 40.0
last_name 40.0
Test1_Score 20.0
sex 20.0
age 20.0
first_name 20.0
dtype: float64
答案 5 :(得分：0)
您正在寻找的解决方案是：
round(df.isnull().mean()*100,2)
这会将百分比四舍五入到小数点后两位
另一种方法是
round((df.isnull().sum()*100)/len(df),2)
但这并不像使用mean（）那样有效。
答案 6 :(得分：0)
如果下面有多个数据框，则该函数可以按百分比计算每列中的缺失值数量
def miss_data(df):
x = ['column_name','missing_data', 'missing_in_percentage']
missing_data = pd.DataFrame(columns=x)
columns = df.columns
for col in columns:
icolumn_name = col
imissing_data = df[col].isnull().sum()
imissing_in_percentage = (df[col].isnull().sum()/df[col].shape[0])*100
missing_data.loc[len(missing_data)] = [icolumn_name, imissing_data, imissing_in_percentage]
print(missing_data)
答案 7 :(得分：0)
通过以下代码，您可以从每一列中获取相应的百分比值。如果您愿意，只需用df切换名称train_data即可。
输入：
In [1]:
all_data_na = (train_data.isnull().sum() / len(train_data)) * 100
all_data_na = all_data_na.drop(all_data_na[all_data_na == 0].index).sort_values(ascending=False)[:30]
missing_data = pd.DataFrame({'Missing Ratio' :all_data_na})
missing_data.head(20)
输出：
Out[1]:
Missing Ratio
left_eyebrow_outer_end_x 68.435239
left_eyebrow_outer_end_y 68.435239
right_eyebrow_outer_end_y 68.279189
right_eyebrow_outer_end_x 68.279189
left_eye_outer_corner_x 67.839410
left_eye_outer_corner_y 67.839410
right_eye_inner_corner_x 67.825223
right_eye_inner_corner_y 67.825223
right_eye_outer_corner_x 67.825223
right_eye_outer_corner_y 67.825223
mouth_left_corner_y 67.811037
mouth_left_corner_x 67.811037
left_eyebrow_inner_end_x 67.796851
left_eyebrow_inner_end_y 67.796851
right_eyebrow_inner_end_y 67.796851
mouth_right_corner_x 67.796851
mouth_right_corner_y 67.796851
right_eyebrow_inner_end_x 67.796851
left_eye_inner_corner_x 67.782664
left_eye_inner_corner_y 67.782664
答案 8 :(得分：0)
对我来说，我是那样做的：
def missing_percent(df):
# Total missing values
mis_val = df.isnull().sum()
# Percentage of missing values
mis_percent = 100 * df.isnull().sum() / len(df)
# Make a table with the results
mis_table = pd.concat([mis_val, mis_percent], axis=1)
# Rename the columns
mis_columns = mis_table.rename(
columns = {0 : 'Missing Values', 1 : 'Percent of Total Values'})
# Sort the table by percentage of missing descending
mis_columns = mis_columns[
mis_columns.iloc[:,1] != 0].sort_values(
'Percent of Total Values', ascending=False).round(2)
# Print some summary information
print ("Your selected dataframe has " + str(df.shape[1]) + " columns.\n"
"There are " + str(mis_columns.shape[0]) +
" columns that have missing values.")
# Return the dataframe with missing information
return mis_columns
答案 9 :(得分：0)
让我们分解您的问题
说明：
代码：
(dhr[fill_cols].isnull().sum()/dhr.shape[0]).round(2).sort_values()
答案 10 :(得分：0)
import numpy as np
import pandas as pd
df = pd.read_csv('https://query.data.world/s/Hfu_PsEuD1Z_yJHmGaxWTxvkz7W_b0')
df.loc[np.isnan(df['Product_Base_Margin']),['Product_Base_Margin']]=df['Product_Base_Margin'].mean()
print(round(100*(df.isnull().sum()/len(df.index)), 2))
答案 11 :(得分：0)
试试这个解决方案
import pandas as pd
df = pd.read_csv('https://query.data.world/s/Hfu_PsEuD1Z_yJHmGaxWTxvkz7W_b0')
print(round(100*(df.isnull().sum()/len(df.index)),2))
答案 12 :(得分：-1)
# Why this chord is not running it shows error
File "<tokenize>", line 19
return mis_val_table_ren_columns
^
IndentationError: unindent does not match any outer indentation level
# check number & percentage of missing value in the columns
def missing_values_table(df):
mis_val = df.isnull().sum() #total missing values
mis_val_percent = 100 * df.isnull().sum() / len(df) #percentage of missing values
mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1) #make a table with the results
mis_val_table_ren_columns = mis_val_table.rename(
columns = {0 : 'Missing Values', 1 : '% of Total Values'}) #rename the columns
# sort the table by percentage of missing value
mis_val_table_ren_columns = mis_val_table_ren_columns[
mis_val_table_ren_columns.iloc[:,1] != 0].sort_values(
'% of Total Values', ascending=False).round(1)
#print same summary information
print ("Your selected dataframe has " + str(df.shape[1]) + " columns.\n"
"There are " + str(mis_val_table_ren_columns.shape[0]) +
" columns that have missing values.")
# return the dataframe with missing information
return mis_val_table_ren_columns
# missing values statistics
missing_values = missing_values_table(data_df)
missing_values.head()