The `pandas` library is indispensable for data scientists, analysts, and anyone working with data in Python. It provides high-performance, easy-to-use data structures and data analysis tools. Below is a concise reference guide for common use cases with `pandas`, formatted in Markdown syntax: # `pandas` Reference Guide ## Installation ``` pip install pandas ``` ## Basic Concepts ### Importing pandas ```python import pandas as pd ``` ### Data Structures - **Series**: One-dimensional array with labels. - **DataFrame**: Two-dimensional, size-mutable, potentially heterogeneous tabular data with labeled axes. ## Creating DataFrames ```python # From a dictionary df = pd.DataFrame({ 'A': [1, 2, 3], 'B': ['a', 'b', 'c'] }) # From a list of lists df = pd.DataFrame([ [1, 'a'], [2, 'b'], [3, 'c'] ], columns=['A', 'B']) ``` ## Reading Data ```python # Read from CSV df = pd.read_csv('filename.csv') # Read from Excel df = pd.read_excel('filename.xlsx') # Other formats include: read_sql, read_json, read_html, read_clipboard, read_pickle, etc. ``` ## Data Inspection ```python # View the first n rows (default 5) df.head() # View the last n rows (default 5) df.tail() # Data summary df.info() # Statistical summary for numerical columns df.describe() ``` ## Data Selection ```python # Select a column df['A'] # Select multiple columns df[['A', 'B']] # Select rows by position df.iloc[0] # First row df.iloc[0:5] # First five rows # Select rows by label df.loc[0] # Row with index label 0 df.loc[0:5] # Rows with index labels from 0 to 5, inclusive ``` ## Data Manipulation ```python # Add a new column df['C'] = [10, 20, 30] # Drop a column df.drop('C', axis=1, inplace=True) # Rename columns df.rename(columns={'A': 'Alpha', 'B': 'Beta'}, inplace=True) # Filter rows filtered_df = df[df['Alpha'] > 1] # Apply a function to a column df['Alpha'] = df['Alpha'].apply(lambda x: x * 2) ``` ## Handling Missing Data ```python # Drop rows with any missing values df.dropna() # Fill missing values df.fillna(value=0) ``` ## Grouping and Aggregating ```python # Group by a column and calculate mean grouped_df = df.groupby('B').mean() # Multiple aggregation functions grouped_df = df.groupby('B').agg(['mean', 'sum']) ``` ## Merging, Joining, and Concatenating ```python # Concatenate DataFrames pd.concat([df1, df2]) # Merge DataFrames pd.merge(df1, df2, on='key') # Join DataFrames df1.join(df2, on='key') ``` ## Saving Data ```python # Write to CSV df.to_csv('filename.csv') # Write to Excel df.to_excel('filename.xlsx') # Other formats include: to_sql, to_json, to_html, to_clipboard, to_pickle, etc. ``` `pandas` is incredibly powerful for data cleaning, transformation, analysis, and visualization. This guide covers the basics, but the library's capabilities are vast and highly customizable to suit complex data manipulation and analysis tasks. --- For high-performance, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive, `pandas` stands out as a crucial tool in Python data science libraries. It provides essential data manipulation capabilities akin to those found in programming languages like R. Here’s a concise reference guide for common use cases with `pandas`, especially tailored for data manipulation and cleaning tasks: # `pandas` Reference Guide ## Installation ``` pip install pandas ``` ## Basic Concepts ### Importing pandas ```python import pandas as pd ``` ### Series and DataFrame - **Series**: One-dimensional labeled array capable of holding data of any type. - **DataFrame**: Two-dimensional labeled data structure with columns of potentially different types. ## Creating DataFrames ```python # From a dictionary df = pd.DataFrame({ 'Column1': [1, 2, 3], 'Column2': ['a', 'b', 'c'] }) # From a list of dictionaries df = pd.DataFrame([ {'Column1': 1, 'Column2': 'a'}, {'Column1': 2, 'Column2': 'b'} ]) # From a CSV file df = pd.read_csv('filename.csv') # From an Excel file df = pd.read_excel('filename.xlsx') ``` ## Basic DataFrame Operations ### Viewing Data ```python # View the first 5 rows df.head() # View the last 5 rows df.tail() # Display the index, columns, and underlying numpy data df.info() ``` ### Data Selection ```python # Select a single column df['Column1'] # Select multiple columns df[['Column1', 'Column2']] # Select rows by position df.iloc[0] # First row # Select rows by label df.loc[0] # Row with index label 0 ``` ### Data Filtering ```python # Rows where Column1 is greater than 1 df[df['Column1'] > 1] ``` ### Adding and Dropping Columns ```python # Adding a new column df['Column3'] = [4, 5, 6] # Dropping a column df.drop('Column3', axis=1, inplace=True) ``` ### Renaming Columns ```python df.rename(columns={'Column1': 'NewName1'}, inplace=True) ``` ### Handling Missing Data ```python # Drop rows with any missing values df.dropna() # Fill missing values df.fillna(value=0) ``` ## Data Manipulation ### Applying Functions ```python # Apply a function to each item df['Column1'] = df['Column1'].apply(lambda x: x * 2) ``` ### Grouping Data ```python # Group by column and calculate mean df.groupby('Column1').mean() ``` ### Merging and Concatenating ```python # Concatenate DataFrames pd.concat([df1, df2]) # Merge DataFrames pd.merge(df1, df2, on='key_column') ``` ### Aggregating Data ```python df.agg({ 'Column1': ['min', 'max', 'mean'], 'Column2': ['sum'] }) ``` ## Working with Time Series ```python # Convert column to datetime df['DateColumn'] = pd.to_datetime(df['DateColumn']) # Set the DateTime column as the index df.set_index('DateColumn', inplace=True) # Resample and aggregate by month df.resample('M').mean() ``` ## Saving Data ```python # Write to a CSV file df.to_csv('new_file.csv') # Write to an Excel file df.to_excel('new_file.xlsx') ``` `pandas` is an indispensable tool for data munging/wrangling. It provides high-level abstractions for complex operations, simplifying tasks like data filtering, transformation, and aggregation. This guide covers foundational operations but barely scratches the surface of `pandas`' capabilities, which are vast and varied, extending well beyond these basics to support complex data manipulation and analysis tasks. ``` Given its powerful and flexible data manipulation capabilities, `pandas` is a cornerstone library for anyone working with data in Python, offering a depth of functionality that covers nearly every aspect of data analysis and manipulation.