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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')