Python - Find the Summary of Statistics of a Pandas DataFrame

The describe() method in Pandas provides a comprehensive statistical summary of numerical columns in a DataFrame. It calculates count, mean, standard deviation, minimum, maximum, and quartiles in one convenient method.

Basic DataFrame Statistics

First, let's create a sample DataFrame and get its statistical summary ?

import pandas as pd

# Create sample data
data = {
    'Car': ['Audi', 'Porsche', 'RollsRoyce', 'BMW', 'Mercedes', 'Lamborghini', 'Audi', 'Mercedes', 'Lamborghini'],
    'Place': ['Bangalore', 'Mumbai', 'Pune', 'Delhi', 'Hyderabad', 'Chandigarh', 'Mumbai', 'Pune', 'Delhi'],
    'UnitsSold': [80, 110, 100, 95, 80, 80, 100, 120, 100]
}

df = pd.DataFrame(data)
print("DataFrame:")
print(df)

DataFrame:
         Car       Place  UnitsSold
0       Audi   Bangalore         80
1    Porsche      Mumbai        110
2 RollsRoyce        Pune        100
3        BMW       Delhi         95
4   Mercedes   Hyderabad         80
5 Lamborghini  Chandigarh        80
6       Audi      Mumbai        100
7   Mercedes        Pune        120
8 Lamborghini       Delhi        100

Getting Statistical Summary

Use describe() to get comprehensive statistics for numerical columns ?

import pandas as pd

data = {
    'Car': ['Audi', 'Porsche', 'RollsRoyce', 'BMW', 'Mercedes', 'Lamborghini', 'Audi', 'Mercedes', 'Lamborghini'],
    'Place': ['Bangalore', 'Mumbai', 'Pune', 'Delhi', 'Hyderabad', 'Chandigarh', 'Mumbai', 'Pune', 'Delhi'],
    'UnitsSold': [80, 110, 100, 95, 80, 80, 100, 120, 100]
}

df = pd.DataFrame(data)

print("Statistical Summary:")
print(df.describe())

Statistical Summary:
       UnitsSold
count   9.000000
mean   96.111111
std    14.092945
min    80.000000
25%    80.000000
50%   100.000000
75%   100.000000
max   120.000000

Understanding the Statistics

The describe() method provides these key statistics ?

• count: Number of non-null values
• mean: Average value
• std: Standard deviation
• min: Minimum value
• 25%: First quartile (25th percentile)
• 50%: Median (50th percentile)
• 75%: Third quartile (75th percentile)
• max: Maximum value

Including All Columns

To include non-numeric columns in the summary, use include='all' ?

import pandas as pd

data = {
    'Car': ['Audi', 'Porsche', 'RollsRoyce', 'BMW', 'Mercedes', 'Lamborghini', 'Audi', 'Mercedes', 'Lamborghini'],
    'Place': ['Bangalore', 'Mumbai', 'Pune', 'Delhi', 'Hyderabad', 'Chandigarh', 'Mumbai', 'Pune', 'Delhi'],
    'UnitsSold': [80, 110, 100, 95, 80, 80, 100, 120, 100]
}

df = pd.DataFrame(data)

print("Complete Summary (All Columns):")
print(df.describe(include='all'))

Complete Summary (All Columns):
              Car     Place  UnitsSold
count           9         9   9.000000
unique          6         6        NaN
top    Lamborghini  Bangalore        NaN
freq            2         1        NaN
mean          NaN       NaN  96.111111
std           NaN       NaN  14.092945
min           NaN       NaN  80.000000
25%           NaN       NaN  80.000000
50%           NaN       NaN 100.000000
75%           NaN       NaN 100.000000
max           NaN       NaN 120.000000

Additional DataFrame Information

Combine describe() with other useful DataFrame methods ?

import pandas as pd

data = {
    'Car': ['Audi', 'Porsche', 'RollsRoyce', 'BMW', 'Mercedes', 'Lamborghini', 'Audi', 'Mercedes', 'Lamborghini'],
    'Place': ['Bangalore', 'Mumbai', 'Pune', 'Delhi', 'Hyderabad', 'Chandigarh', 'Mumbai', 'Pune', 'Delhi'],
    'UnitsSold': [80, 110, 100, 95, 80, 80, 100, 120, 100]
}

df = pd.DataFrame(data)

print("DataFrame Shape:", df.shape)
print("\nFirst 5 rows:")
print(df.head())
print("\nStatistical Summary:")
print(df.describe())

DataFrame Shape: (9, 3)

First 5 rows:
         Car       Place  UnitsSold
0       Audi   Bangalore         80
1    Porsche      Mumbai        110
2 RollsRoyce        Pune        100
3        BMW       Delhi         95
4   Mercedes   Hyderabad         80

Statistical Summary:
       UnitsSold
count   9.000000
mean   96.111111
std    14.092945
min    80.000000
25%    80.000000
50%   100.000000
75%   100.000000
max   120.000000

Conclusion

The describe() method provides essential statistical insights for numerical data analysis. Use include='all' to analyze both numeric and categorical columns. Combine it with shape and head() for comprehensive data exploration.