Pandas cheatsheet

1. Creating a Pandas Series

import pandas as pd

s = pd.Series([10, 20, 30, 40], index=['a', 'b', 'c', 'd'])
print(s)

2. Creating a Pandas DataFrame

data = {'Name': ['John', 'Anna', 'Peter', 'Linda', 'Tom'],
        'Age': [28, 24, 35, 32, 28],
        'City': ['New York', 'Paris', 'Berlin', 'London', 'New York']}

df = pd.DataFrame(data, index = ["a9", "b5", "d2", "r1", "x6"])
print(df)

3. Reading and Writing to CSV

• Reading/writing CSV:

  df = pd.read_csv('data.csv')
  
  df.to_csv('output.csv', index=False)
  

4. Accessing Rows and Columns (Indexing and Slicing)

• Accessing rows:

  df.iloc[3]           # Access by position
  df.loc['b5']         # Access by index label
  

• Multiple indices:

  df.loc[["b5", "d2"]]  
  df.iloc[[2, 3]]
  

• Slicing rows:

  df.iloc[1:4:2]       # Slicing from row 1 to 4, with steps of 2
  

• Accessing columns:

  df['Name']           # Single column -> Series
  df[['Name', 'Age']]  # Multiple columns -> DF
  

5. statistics

• Statistical info of columns:

  df['Age'].mean()
  df['Age'].median()
  df['Age'].sum()  
  df['Age'].min()  
  df['Age'].max()  
  df['Age'].var()  
  df['Age'].std()
  df['Age'].count()  # Count non-null values
  

6. Filtering Data

• Single condition: Filter rows based on a single condition.

  df[df['Age'] > 30]
  

• Filter with multiple conditions: You can combine multiple conditions using & (and) or | (or), and parentheses ().

  # Example: Filter rows where Age > 30 and City is 'New York'
  filtered_df = df[(df['Age'] > 30) & (df['City'] == 'New York')]
  
  # Example: Filter rows where Age < 30 or City is 'Paris'
  filtered_df = df[(df['Age'] < 30) | (df['City'] == 'Paris')]
  

7. Grouping and Looping Over Groups

• Grouping by ‘City’ and Looping Over Groups:

  grouped = df.groupby('City')
  
  for city, group in grouped:
      print(f"\nCity: {city}")
      print(group)
  

8. Common Aggregation Functions

• Aggregation after groupby:

  grouped['Age'].mean()      
  grouped['Age'].sum()       
  grouped['Age'].count()    
  grouped['Age'].max()      
  grouped['Age'].min()       
  grouped['Age'].median()    
  grouped['Age'].std()       
  grouped['Age'].var()      
  

9. Column Operations: Adding, Multiplying, etc.

• Arithmetic operations on columns:

  # Adding a new column by combining other columns
  df['Salary'] = [50000, 60000, 75000, 65000, 55000]
  df['Bonus'] = df['Salary'] * 0.10  
  
  # Adding, subtracting, multiplying columns
  df['Total Compensation'] = df['Salary'] + df['Bonus']
  df['Years Until Retirement'] = 65 - df['Age']  
  

10. Sorting Data

• Sort by a single column:

  df.sort_values(by='Age')  
  

• Sort by multiple columns:

  df.sort_values(by=['Age', 'Salary'], ascending=[True, False])