Pandas_CheatSheet.md (#313)

* Create Pandas_CheatSheet.md

* Pandas_CheatSheet.md

Description:This pull request introduces a variety of basic operations using the Pandas library to demonstrate DataFrame and Series functionalities. The additions include creating DataFrames and Series, viewing data, selecting and filtering data, basic data manipulations (such as adding and removing columns), data aggregation, file input/output operations, and basic text and data type manipulations. This will serve as a foundational script for our future assignments and projects where data manipulation and analysis are required.

DIGT1161
Members and Contributions:
Mahjabin: Focused on file input/output functions and data filtering methods.
Alicia: Worked on data creation, viewing, and basic DataFrame operations.
Sienna: Handled text manipulation functions and data type conversions.
Steeve: Implemented statistical methods and basic plotting features.

This collective effort has ensured a comprehensive introduction to handling data using Pandas, tailored to assist our class peers in getting up to speed with Python data manipulation.

Pandas_CheatSheet.md

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+
+
+*Title: Introduction of Basic Pandas Operations*
+
+# Import the pandas library
+import pandas as pd
+
+# Create an empty DataFrame
+pd.DataFrame()
+
+# Create a DataFrame from a dictionary of lists
+# Each key becomes a column name, and the list elements are the column values
+pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
+
+# Create a Series from a list
+pd.Series([1, 2, 3, 4, 5])
+
+# View the first few rows of a DataFrame
+# Helps to quickly inspect the beginning of large DataFrames
+df.head()
+
+# View the last few rows of a DataFrame
+# Useful for checking the end portions of large DataFrames
+df.tail()
+
+# Select a single column, returns a Series
+df['column_name']
+
+# Select multiple columns, returns a DataFrame
+df[['column1', 'column2']]
+
+# Select rows by position (integer-location based)
+df.iloc[0]  # First row
+df.iloc[0:5]  # First five rows
+
+# Select a specific value by row and column position
+df.iloc[0, 0]  # Value in first row and first column
+
+# Filter rows based on column values
+df[df['column'] > 10]
+
+# Get basic statistics for numerical columns
+df.describe()
+
+# Get the data types of columns
+df.dtypes
+
+# Check for missing values
+df.isnull()
+
+# Drop rows with any missing values
+df.dropna()
+
+# Fill missing values with a specified value
+df.fillna(value)
+
+# Create a new column
+df['new_column'] = [value1, value2, value3]
+
+# Drop a column
+df.drop('column_name', axis=1, inplace=True)
+
+# Rename columns
+df.rename(columns={'old_name': 'new_name'}, inplace=True)
+
+# Sum of a column's values
+df['column'].sum()
+
+# Average (mean) of a column's values
+df['column'].mean()
+
+# Converts all characters in the column to uppercase
+df[‘text_coloumn’].str.upper()
+
+# Converts all characters in the column to lowercase
+df[‘text_column’].str.lower()
+
+# Removes leading and trailing whitespace from each entry in the column
+df[‘text_column’].str.strip()
+
+# Aligns text to the left
+df[‘text_column’].str.ljust(width)
+
+# Aligns text to the right
+df[‘text_column’].str.rjust(width)
+
+# Aligns text to the center
+df[‘text_column’].str.center(width)
+
+# Read data from a CSV file into a DataFrame
+pd.read_csv('filename.csv') #Other types: read_json, read_html, read_xml, read_excel
+
+# Write data from a DataFrame to a CSV file
+df.to_csv('filename.csv', index=False) #Other types: to_json, to_html, to_xml, to_excel
+
+# Sort DataFrame by a column
+df.sort_values(by='column_name')
+
+# Get unique values from a column
+df['column_name'].unique()
+
+# Get the number of unique values in a column
+df['column_name'].nunique()
+
+# Get the count of occurrences of each unique value in a column
+df['column_name'].value_counts()
+
+# Set a column as the index of the DataFrame
+df.set_index('column_name', inplace=True)
+
+# Reset the index, turning it back into a column
+df.reset_index(inplace=True)
+
+# Convert a column's data type
+df['column_name'] = df['column_name'].astype('new_type') #e.g. “float32”
+
+# Basic plotting (requires matplotlib)
+df['column_name'].plot()
+
+# Save a plot to a file (requires matplotlib)
+df['column_name'].plot().figure.savefig('plot.png')