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Python Pandas: How to Ensure 'DataFrame.describe()' Shows All Columns

Learn how to make Pandas DataFrame.describe() include all columns by using the include parameter and adjusting display settings for complete statistical visibility.

The DataFrame.describe() method in Pandas is a powerful tool for quickly generating summary statistics for your data. However, you might sometimes find that its output doesn't include all the columns from your DataFrame, or that the displayed output itself is truncated.

This guide will thoroughly address these common scenarios, explaining why describe() might omit columns by default and how Pandas display settings can affect output visibility. You'll learn how to use the include parameter to customize which columns are described and how to adjust display options to see the full summary, even for wide DataFrames.

The Default Behavior of DataFrame.describe()

By default, when you call df.describe() on a Pandas DataFrame, it generates descriptive statistics only for columns with numeric data types (e.g., int64, float64). Columns with non-numeric types, such as strings (object) or datetimes, are excluded from this default summary.

Let's consider a sample DataFrame:

import pandas as pd

df = pd.DataFrame({
    'employee_name': ['Alice Smith', 'Robert Jones', 'Charles Brown', 'Diana Wilson'],
    'department': ['HR', 'IT', 'HR', 'Sales'],
    'years_experience': [5, 10, 3, 8],
    'annual_salary': [75000.0, 120000.0, 65000.0, 95000.0]
})

print("Original DataFrame:")
print(df)
print()

print("Default df.describe() output:")
print(df.describe())

Output:

Original DataFrame:
   employee_name department  years_experience  annual_salary
0    Alice Smith         HR                 5        75000.0
1   Robert Jones         IT                10       120000.0
2  Charles Brown         HR                 3        65000.0
3   Diana Wilson      Sales                 8        95000.0

Default df.describe() output:
       years_experience  annual_salary
count          4.000000       4.000000
mean           6.500000   88750.000000
std            3.109126   24281.337141
min            3.000000   65000.000000
25%            4.500000   72500.000000
50%            6.500000   85000.000000
75%            8.500000  101250.000000
max           10.000000  120000.000000

Solution 1: Using the include Parameter in describe()

The include parameter of df.describe() allows you to control which column types are included in the summary.

Including All Columns with include='all'

To get descriptive statistics for all columns, regardless of their data type, set include='all'.

import pandas as pd

df = pd.DataFrame({
    'employee_name': ['Alice Smith', 'Robert Jones', 'Charles Brown', 'Diana Wilson'],
    'department': ['HR', 'IT', 'HR', 'Sales'],
    'years_experience': [5, 10, 3, 8],
    'annual_salary': [75000.0, 120000.0, 65000.0, 95000.0]
})

all_columns_description = df.describe(include='all')
print("df.describe(include='all') output:")
print(all_columns_description)

Output:

       employee_name department  years_experience  annual_salary
count              4          4          4.000000       4.000000
unique             4          3               NaN            NaN
top      Alice Smith         HR               NaN            NaN
freq               1          2               NaN            NaN
mean             NaN        NaN          6.500000   88750.000000
std              NaN        NaN          3.109126   24281.337141
min              NaN        NaN          3.000000   65000.000000
25%              NaN        NaN          4.500000   72500.000000
50%              NaN        NaN          6.500000   85000.000000
75%              NaN        NaN          8.500000  101250.000000
max              NaN        NaN         10.000000  120000.000000

Understanding the Output with include='all'

When include='all' is used:

  • For numeric columns, you get count, mean, std, min, 25% (1st quartile), 50% (median), 75% (3rd quartile), and max.
  • For non-numeric columns (like strings/objects), you get count, unique (number of distinct values), top (most frequent value), and freq (frequency of the top value).
  • Metrics not applicable to a data type will show as NaN.

Describing Specific Data Types (e.g., np.number, object)

You can also pass a list of data types to include to summarize only columns of those types.

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'employee_name': ['Alice Smith', 'Robert Jones', 'Charles Brown', 'Diana Wilson'],
    'department': ['HR', 'IT', 'HR', 'Sales'],
    'years_experience': [5, 10, 3, 8],
    'annual_salary': [75000.0, 120000.0, 65000.0, 95000.0]
})

numeric_description = df.describe(include=[np.number])
print("df.describe(include=[np.number]):")
print(numeric_description)
print()

object_description = df.describe(include=['object'])
print("df.describe(include=['object']):")
print(object_description)

Output:

df.describe(include=[np.number]):
       years_experience  annual_salary
count          4.000000       4.000000
mean           6.500000   88750.000000
std            3.109126   24281.337141
min            3.000000   65000.000000
25%            4.500000   72500.000000
50%            6.500000   85000.000000
75%            8.500000  101250.000000
max           10.000000  120000.000000

df.describe(include=['object']):
       employee_name department
count              4          4
unique             4          3
top      Alice Smith         HR
freq               1          2

Describing a Single Column

You can call .describe() directly on a single Series (column) as well.

import pandas as pd

df = pd.DataFrame({
    'employee_name': ['Alice Smith', 'Robert Jones', 'Charles Brown', 'Diana Wilson'],
    'department': ['HR', 'IT', 'HR', 'Sales'],
    'years_experience': [5, 10, 3, 8],
    'annual_salary': [75000.0, 120000.0, 65000.0, 95000.0]
})

salary_description = df['annual_salary'].describe()
print("Description of 'annual_salary' column:")
print(salary_description)
print()

department_description = df['department'].describe()
print("Description of 'department' column:")
print(department_description)

Output:

Description of 'annual_salary' column:
count         4.000000
mean      88750.000000
std       24281.337141
min       65000.000000
25%       72500.000000
50%       85000.000000
75%      101250.000000
max      120000.000000
Name: annual_salary, dtype: float64

Description of 'department' column:
count      4
unique     3
top       HR
freq       2
Name: department, dtype: object

Solution 2: Adjusting Pandas Display Options for Wide Output

Even if describe(include='all') generates statistics for all columns, Pandas might still truncate the display of these columns in your console if the DataFrame is too wide. This is controlled by display.max_columns.

Setting display.max_columns Globally

You can set this option to None to tell Pandas to display all columns without truncation.

import pandas as pd

df = pd.DataFrame({
    'employee_name': ['Alice Smith', 'Robert Jones', 'Charles Brown', 'Diana Wilson'],
    'department': ['HR', 'IT', 'HR', 'Sales'],
    'years_experience': [5, 10, 3, 8],
    'annual_salary': [75000.0, 120000.0, 65000.0, 95000.0]
})


pd.set_option('display.max_columns', None)

print("Original DataFrame (with display.max_columns=None):")
print(df)
print()

all_columns_description_full_display = df.describe(include='all')
print("df.describe(include='all') output (with display.max_columns=None):")
print(all_columns_description_full_display)

Output:

Original DataFrame (with display.max_columns=None):
   employee_name department  years_experience  annual_salary
0    Alice Smith         HR                 5        75000.0
1   Robert Jones         IT                10       120000.0
2  Charles Brown         HR                 3        65000.0
3   Diana Wilson      Sales                 8        95000.0

df.describe(include='all') output (with display.max_columns=None):
       employee_name department  years_experience  annual_salary
count              4          4          4.000000       4.000000
unique             4          3               NaN            NaN
top      Alice Smith         HR               NaN            NaN
freq               1          2               NaN            NaN
mean             NaN        NaN          6.500000   88750.000000
std              NaN        NaN          3.109126   24281.337141
min              NaN        NaN          3.000000   65000.000000
25%              NaN        NaN          4.500000   72500.000000
50%              NaN        NaN          6.500000   85000.000000
75%              NaN        NaN          8.500000  101250.000000
max              NaN        NaN         10.000000  120000.000000

Using pd.option_context for Temporary Settings

If you only want to change the display setting for a specific block of code, use pd.option_context.

import pandas as pd

df = pd.DataFrame({
    'employee_name': ['Alice Smith', 'Robert Jones', 'Charles Brown', 'Diana Wilson'],
    'department': ['HR', 'IT', 'HR', 'Sales'],
    'years_experience': [5, 10, 3, 8],
    'annual_salary': [75000.0, 120000.0, 65000.0, 95000.0]
})

with pd.option_context('display.max_columns', None):
    print("df.describe(include='all') output (within option_context):")
    print(df.describe(include='all'))
    print()

print("df.describe(include='all') output (after option_context, may be truncated if wide):")
print(df.describe(include='all'))

Output:

df.describe(include='all') output (within option_context):
       employee_name department  years_experience  annual_salary
count              4          4          4.000000       4.000000
unique             4          3               NaN            NaN
top      Alice Smith         HR               NaN            NaN
freq               1          2               NaN            NaN
mean             NaN        NaN          6.500000   88750.000000
std              NaN        NaN          3.109126   24281.337141
min              NaN        NaN          3.000000   65000.000000
25%              NaN        NaN          4.500000   72500.000000
50%              NaN        NaN          6.500000   85000.000000
75%              NaN        NaN          8.500000  101250.000000
max              NaN        NaN         10.000000  120000.000000

df.describe(include='all') output (after option_context, may be truncated if wide):
       employee_name department  years_experience  annual_salary
count              4          4          4.000000       4.000000
unique             4          3               NaN            NaN
top      Alice Smith         HR               NaN            NaN
freq               1          2               NaN            NaN
mean             NaN        NaN          6.500000   88750.000000
std              NaN        NaN          3.109126   24281.337141
min              NaN        NaN          3.000000   65000.000000
25%              NaN        NaN          4.500000   72500.000000
50%              NaN        NaN          6.500000   85000.000000
75%              NaN        NaN          8.500000  101250.000000
max              NaN        NaN         10.000000  120000.000000

Combining Solutions for Full Visibility

For complete visibility of all descriptive statistics for all columns:

  1. Use df.describe(include='all').
  2. Ensure pd.options.display.max_columns is set appropriately (e.g., to None) if your resulting describe() output DataFrame is very wide.

Conclusion

When DataFrame.describe() doesn't show all your columns, the reason is usually twofold:

  1. Data Type Filtering: By default, it only processes numeric columns. Use the include='all' (or specify data types) parameter to control which columns are summarized.
  2. Display Truncation: For wide DataFrames, Pandas' display settings might hide columns. Use pd.set_option('display.max_columns', None) or pd.option_context to ensure all generated columns are visible in the output. By understanding and applying these solutions, you can effectively use describe() to get a comprehensive statistical overview of all data within your Pandas DataFrames.
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