How to get maximum length of each column in the data frame using pandas python

One solution is to use numpy.vectorize. This may be more efficient than pandas-based solutions.

You can use pd.DataFrame.select_dtypes to select object columns.

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': ['abc', 'de', 'abcd'],
                   'B': ['a', 'abcde', 'abc'],
                   'C': [1, 2.5, 1.5]})

measurer = np.vectorize(len)

Max length for all columns

res1 = measurer(df.values.astype(str)).max(axis=0)

array([4, 5, 3])

Max length for object columns

res2 = measurer(df.select_dtypes(include=[object]).values.astype(str)).max(axis=0)

array([4, 5])

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Or if you need output as a dictionary:

res1 = dict(zip(df, measurer(df.values.astype(str)).max(axis=0)))

{'A': 4, 'B': 5, 'C': 3}

df_object = df.select_dtypes(include=[object])
res2 = dict(zip(df_object, measurer(df_object.values.astype(str)).max(axis=0)))

{'A': 4, 'B': 5}

Some great answers here and I would like to contribute mine

Solution:

dict([(v, df[v].apply(lambda r: len(str(r)) if r!=None else 0).max())for v in df.columns.values])

Explanation:

#convert tuple to dictionary
dict( 
    [
        #create a tuple such that (column name, max length of values in column)
        (v, df[v].apply(lambda r: len(str(r)) if r!=None else 0).max()) 
            for v in df.columns.values #iterates over all column values
    ])

Sample output

{'name': 4, 'DoB': 10, 'Address': 2, 'comment1': 21, 'comment2': 17}

You can use min max after using str and len method

df["A"].str.len().max()
df["A"].str.len().min()

df["Column Name"].str.len().max()
df["Column Name"].str.len().min()