Pandas: counting the proportion of zeros in rows and columns of data

Question

I have this code below. It's amazing to me that it works for columns, not rows.

import pandas as pd

def summarizing_data_variables(df):
    numberRows=size(df['ID'])
    numberColumns=size(df.columns)
    summaryVariables=np.empty([numberColumns,2], dtype =  np.dtype('a50'))    
    cont=-1    
    for column in df.columns:
        cont=cont+1
        summaryVariables[cont][0]=column
        summaryVariables[cont][1]=size(df[df[column].isin([0])][column])/(1.0*numberRows)
    print summaryVariables

def summarizing_data_users(fileName):
    print "Sumarizing users..."   
    numberRows=size(df['ID'])
    numberColumns=size(df.columns)      
    summaryVariables=np.empty([numberRows,2], dtype =  np.dtype('a50'))    
    cont=-1

    for row in df['ID']:
        cont=cont+1
        summaryVariables[cont][0]=row
        dft=df[df['ID']==row]
        proportionZeros=(size(dft[dft.isin([0])])-1)/(1.0*(numberColumns-1)) # THe -1 is used to not count the ID column
        summaryVariables[cont][1]=proportionZeros
    print summaryVariables


if __name__ == '__main__':

    df = pd.DataFrame([[1, 2, 3], [2, 5, 0.0],[3,4,5]])
    df.columns=['ID','var1','var2']
    print df

    summarizing_data_variables(df)
    summarizing_data_users(df) 

Output:

   ID  var1  var2
0   1     2     3
1   2     5     0
2   3     4     5
[['ID' '0.0']
 ['var1' '0.0']
 ['var2' '0.333333333333']]
Sumarizing users...
[['1' '1.0']
 ['2' '1.0']
 ['3' '1.0']]

I expected for users:

Sumarizing users...
[['1' '0.0']
 ['2' '0.5']
 ['3' '0.0']]

It seems that the problem is in this line:

Tsp [dft.isin ([0])]

It does not limit dft to "True", as in the first case.

Can you help me? (1) How to fix part of users (ROWS) (second function above)? (2) Is this the most effective method for this? [My database is very large]

EDIT:

summaryizing_data_variables (df) . Id ( ), var1 ( , ), var2 ( 1/3). 2D numpy.array, , - .

summaryizing_data_users , . .

Answer 1

funtion:

print(df[df == 0].count(axis=1)/len(df.columns))

():

print('rows')
print(df[df == 0].count(axis=1)/len(df.columns))
print('cols')
print(df[df == 0].count(axis=0)/len(df.index))

( ):

ID  var1  var2
1     2     3
2     5     0
3     4     5
4    10    10
5    1      0

:

rows
ID
1    0.0
2    0.5
3    0.0
4    0.0
5    0.5
dtype: float64
cols
var1    0.0
var2    0.4
dtype: float64