SciPy with Pandas for data handling

Introduction

SciPy helps with math and stats. Pandas helps organize data in tables. Together, they make data analysis easier and faster.

You have a table of numbers and want to find averages or correlations.

You want to clean and organize data before doing math or stats.

You need to run scientific calculations on data stored in tables.

You want to combine easy data handling with powerful math tools.

Syntax

SciPy

import pandas as pd
from scipy import stats

# Create a DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Use SciPy function on a column
result = stats.describe(df['A'])

Use pandas to create and manage tables called DataFrames.

Use scipy.stats for statistical functions on data columns.

Examples

Calculate average height with Pandas and correlation between height and weight with SciPy.

SciPy

import pandas as pd
from scipy import stats

data = {'height': [170, 180, 175], 'weight': [65, 80, 75]}
df = pd.DataFrame(data)

mean_height = df['height'].mean()
correlation = stats.pearsonr(df['height'], df['weight'])

Clean data with Pandas before using SciPy stats functions.

SciPy

import pandas as pd
from scipy import stats

# Data with some missing values
data = {'score': [90, 85, None, 88, 92]}
df = pd.DataFrame(data)

# Drop missing values before stats
df_clean = df.dropna()
result = stats.describe(df_clean['score'])

Sample Program

This program shows how to use Pandas to organize data and SciPy to get detailed statistics.

SciPy

import pandas as pd
from scipy import stats

# Create a DataFrame with exam scores
scores = {'math': [88, 92, 79, 93, 85], 'english': [84, 90, 78, 88, 86]}
df = pd.DataFrame(scores)

# Calculate mean and standard deviation for math scores
mean_math = df['math'].mean()
std_math = df['math'].std()

# Use SciPy to get detailed stats for english scores
english_stats = stats.describe(df['english'])

print(f"Math mean: {mean_math:.2f}")
print(f"Math std dev: {std_math:.2f}")
print(f"English stats: nobs={english_stats.nobs}, minmax={english_stats.minmax}, mean={english_stats.mean:.2f}, variance={english_stats.variance:.2f}")

Important Notes

Always check for missing data in Pandas before using SciPy functions.

SciPy stats functions often need clean numeric data from Pandas columns.

Pandas and SciPy work well together for quick and powerful data analysis.

Summary

SciPy provides math and stats tools.

Pandas organizes data in tables called DataFrames.

Use Pandas to prepare data, then SciPy to analyze it.