EDS 217 Cheatsheet

Introduction

This cheatsheet provides a quick reference for common operations on Pandas DataFrames. It’s designed for beginning data science students who are just starting to work with Pandas.

Importing Pandas

Always start by importing pandas:

Code

import pandas as pd

Creating a DataFrame

From a dictionary

Code

data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'City': ['New York', 'San Francisco', 'Los Angeles']}
df = pd.DataFrame(data)
print(df)

      Name  Age           City
0    Alice   25       New York
1      Bob   30  San Francisco
2  Charlie   35    Los Angeles

From a CSV file

Code

# Here's an example csv file we can use for read_csv:
from io import StringIO
# Create a CSV string
csv_data = """
Name,Age,City
Alice,25,New York
Bob,30,San Francisco
Charlie,35,Los Angeles
"""

# Use StringIO to create a file-like object
csv_file = StringIO(csv_data.strip())

# Read the CSV data into a DataFrame
df = pd.read_csv(csv_file)
print(df)

      Name  Age           City
0    Alice   25       New York
1      Bob   30  San Francisco
2  Charlie   35    Los Angeles

Basic DataFrame Information

Code

# Display the first few rows
print(df.head())

# Get basic information about the DataFrame
print(df.info())

# Get summary statistics
print(df.describe())

# Get column names
print(df.columns)

# Get dimensions (rows, columns)
print(df.shape)

      Name  Age           City
0    Alice   25       New York
1      Bob   30  San Francisco
2  Charlie   35    Los Angeles
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype 
---  ------  --------------  ----- 
 0   Name    3 non-null      object
 1   Age     3 non-null      int64 
 2   City    3 non-null      object
dtypes: int64(1), object(2)
memory usage: 204.0+ bytes
None
        Age
count   3.0
mean   30.0
std     5.0
min    25.0
25%    27.5
50%    30.0
75%    32.5
max    35.0
Index(['Name', 'Age', 'City'], dtype='object')
(3, 3)

Selecting Data

Selecting columns

Code

# Select a single column
age_column = df['Age']

# Select multiple columns
subset = df[['Name', 'City']]

Selecting rows

Code

# Select rows by index
first_row = df.loc[0]

# Select rows by condition
adults = df[df['Age'] >= 18]

Basic Data Manipulation

Adding a new column

Code

df['Is Adult'] = df['Age'] >= 18

Renaming columns

Code

df = df.rename(columns={'Name': 'Full Name'})

Handling missing values

Code

# Drop rows with any missing values
df_cleaned = df.dropna()

# Fill missing values
df_filled = df.fillna(0)  # Fills with 0

Basic Calculations

Code

# Calculate mean age
mean_age = df['Age'].mean()

# Count occurrences
city_counts = df['City'].value_counts()

Grouping and Aggregation

Code

# Group by city and calculate mean age
city_age = df.groupby('City')['Age'].mean()

Sorting

Code

# Sort by Age in descending order
df_sorted = df.sort_values('Age', ascending=False)

Saving a DataFrame

Code

# Save to CSV
df.to_csv('output.csv', index=False)

Further Learning

For more advanced operations and in-depth explanations, check out these resources:

Pandas Official Documentation
10 Minutes to Pandas
Python for Data Analysis by Wes McKinney
Pandas Cheat Sheet

Remember, practice is key! Try these operations with different datasets to become more comfortable with Pandas DataFrames.