如何使用 Pandas Python 计算一行中的列数

Question

I am writing a program where I want to count the number of columns in each row as each file has a different number of columns. It means I want to check if any row is missing a cell, and if it does, then I want to highlight the cell number. I am using pandas for that to read the file. I have multiple gzip files which contain another CSV file. My code for reading the files:

#running this under loop
data = pd.read_csv(files,
    compression='gzip'
    on_bad_lines='warn'
    low_memory=False,
    sep=r'|',
    header=None,
    na_values=['NULL',' ','NaN'],
    keep_default_na = False
    )

I checked StackOverflow but there's no answer related to this situation. I would be really glad if someone can help me out here.

Answer 1

Not sure if i'm interpreting this right but if you want to count the number of columns in each pandas dataframe within a loop , there are plenty of options.

1) data.shape[1]
2) len(data.columns)
3) len(list(data))

Here is a minimal reproducibility code. Replace "data = pd.DataFrame(dict)" with "data = pd.read_csv(...)"

# Import Required Libraries
import pandas as pd
import numpy as np

# Create dictionaries for the dataframe
dict1 = {'Name': ['Anne', 'Bob', 'Carl'], 
         'Age': [22, 20, 22], 
         'Marks': [90, 84, 82]}

dict2 = {'Name': ['Dan', 'Ely', 'Fan'], 
         'Age': [52, 30, 12], 
         'Marks': [40, 54, 42]}

for i in [dict1, dict2]:
    # Read data
    data = pd.DataFrame(dict1)

    # Get columns
    shape = data.shape # (3,3)
    col = shape[1] # 3
  
    # Printing Number of columns
    print(f'Number of columns for file <>: {col}')

"This works fine, but after trying your suggestion I am getting the total number of columns that we have in our data frame. I want to print the number of columns each row contains. For eg: S.no Name 1 Adam 2 George 3 NULL so, 1st row will print 2, the second will be 2, but the third will print one."
– Ramoxx

Below is the updated answer(s) for your specification

Get counts of non-nulls for each row

data.apply(lambda x: x.count(), axis=1)

data:

  A   B   C
0:  1   2   3
1:  2   nan nan
2:  nan nan nan

output:

0:  3
1:  1
2:  0

Add counts of non-nulls for each row into dataframe

data['count'] = data.apply(lambda x: x.count(), axis=1)

result:

    A   B   C   count
0:  1   1   3   3
1:  2   nan nan 1
2:  nan nan nan 0