Complete Pandas Tutorial

# Install: pip install pandas
import pandas as pd
import numpy as np
print("Pandas version:", pd.__version__)

# Series - like a column in Excel
ages = pd.Series([25, 30, 35], index=['Alice', 'Bob', 'Charlie'])
print(ages)
print("Alice's age:", ages['Alice'])
print("Average age:", ages.mean())

# DataFrame - like a spreadsheet
data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['NYC', 'LA', 'Chicago']
}
df = pd.DataFrame(data)
print(df)

# From dictionary
df1 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})

# From list of lists
df2 = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B'])

# From NumPy array
df3 = pd.DataFrame(np.random.randn(3, 2), columns=['X', 'Y'])
print("Random data:\n", df3)

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie', 'Diana'],
    'Age': [25, 30, 35, 28],
    'Salary': [50000, 60000, 70000, 55000]
})

print(df.head(2))      # First 2 rows
print(df.tail(2))      # Last 2 rows
print(df.info())       # Data types and info
print(df.describe())   # Statistical summary

print("Shape:", df.shape)           # (4, 3)
print("Columns:", df.columns.tolist())
print("Data types:\n", df.dtypes)
print("Memory usage:", df.memory_usage().sum())

# Single column
names = df['Name']
print(names)

# Multiple columns
subset = df[['Name', 'Age']]
print(subset)

# Column operations
print("Average salary:", df['Salary'].mean())

# By position
print(df.iloc[0])      # First row
print(df.iloc[0:2])    # First 2 rows

# By label/condition
print(df.loc[df['Age'] > 30])  # Rows where Age > 30
print(df.loc[0, 'Name'])       # Specific cell

# Single condition
young = df[df['Age'] < 30]
print("Young employees:\n", young)

# Multiple conditions
high_earners = df[(df['Age'] > 25) & (df['Salary'] > 55000)]
print("High earners:\n", high_earners)

# Using isin()
selected = df[df['Name'].isin(['Alice', 'Bob'])]
print("Selected people:\n", selected)

# String operations
df['Email'] = ['[email protected]', '[email protected]', 
               '[email protected]', '[email protected]']

gmail_users = df[df['Email'].str.contains('gmail')]
print("Gmail users:\n", gmail_users)

# Add new column
df['Bonus'] = df['Salary'] * 0.1
df['Full_Name'] = df['Name'] + ' Smith'

# Modify existing column
df['Age'] = df['Age'] + 1  # Everyone gets older

# Conditional assignment
df['Category'] = df['Age'].apply(lambda x: 'Senior' if x > 30 else 'Junior')
print(df[['Name', 'Age', 'Category']])

# Drop columns
df_clean = df.drop(['Bonus', 'Full_Name'], axis=1)

# Drop rows
df_filtered = df.drop(0)  # Drop first row

# Drop by condition
df_no_seniors = df[df['Category'] != 'Senior']
print("No seniors:\n", df_no_seniors)

# Create data with missing values
df_missing = pd.DataFrame({
    'A': [1, 2, None, 4],
    'B': [5, None, 7, 8],
    'C': [9, 10, 11, None]
})

print("Missing values:\n", df_missing.isnull())
print("Count missing:\n", df_missing.isnull().sum())

# Drop missing values
df_dropped = df_missing.dropna()

# Fill missing values
df_filled = df_missing.fillna(0)  # Fill with 0
df_mean_filled = df_missing.fillna(df_missing.mean())  # Fill with mean

print("Filled with mean:\n", df_mean_filled)

# Sample data
sales = pd.DataFrame({
    'Region': ['North', 'South', 'North', 'South', 'North'],
    'Product': ['A', 'A', 'B', 'B', 'A'],
    'Sales': [100, 150, 200, 120, 180]
})

# Group by region
region_sales = sales.groupby('Region')['Sales'].sum()
print("Sales by region:\n", region_sales)

# Multiple aggregations
stats = sales.groupby('Region')['Sales'].agg(['sum', 'mean', 'count'])
print("Region stats:\n", stats)

# Group by multiple columns
multi_group = sales.groupby(['Region', 'Product'])['Sales'].sum()
print("Sales by region and product:\n", multi_group)

# Pivot table
pivot = sales.pivot_table(values='Sales', 
                         index='Region', 
                         columns='Product', 
                         aggfunc='sum')
print("Pivot table:\n", pivot)

df1 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df2 = pd.DataFrame({'A': [5, 6], 'B': [7, 8]})

# Vertical concatenation (stack rows)
vertical = pd.concat([df1, df2], ignore_index=True)
print("Vertical:\n", vertical)

# Horizontal concatenation (side by side)
horizontal = pd.concat([df1, df2], axis=1)
print("Horizontal:\n", horizontal)

employees = pd.DataFrame({'ID': [1, 2, 3], 'Name': ['Alice', 'Bob', 'Charlie']})
salaries = pd.DataFrame({'ID': [1, 2, 4], 'Salary': [50000, 60000, 70000]})

# Inner join (only matching records)
merged = pd.merge(employees, salaries, on='ID', how='inner')
print("Inner join:\n", merged)

# Left join (all from left table)
left_merged = pd.merge(employees, salaries, on='ID', how='left')
print("Left join:\n", left_merged)

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

# Read from CSV
df_loaded = pd.read_csv('data.csv')
print("Loaded data:\n", df_loaded.head())

# Read with options
df_custom = pd.read_csv('data.csv', usecols=['Name', 'Age'])
print("Selected columns:\n", df_custom)

# Save to Excel
df.to_excel('data.xlsx', sheet_name='Employees', index=False)

# Read from Excel
df_excel = pd.read_excel('data.xlsx', sheet_name='Employees')

# Multiple sheets
with pd.ExcelWriter('multi_sheet.xlsx') as writer:
    df.to_excel(writer, sheet_name='Sheet1', index=False)
    df.to_excel(writer, sheet_name='Sheet2', index=False)

# Create date range
dates = pd.date_range('2024-01-01', periods=5, freq='D')
df_time = pd.DataFrame({'Date': dates, 'Value': [1, 2, 3, 4, 5]})

# Set date as index
df_time.set_index('Date', inplace=True)
print("Time series:\n", df_time)

# Date filtering
recent = df_time['2024-01-03':]
print("Recent data:\n", recent)

# Parse dates from strings
df_dates = pd.DataFrame({
    'Date_String': ['2024-01-01', '2024-01-02', '2024-01-03'],
    'Value': [10, 20, 30]
})

df_dates['Date'] = pd.to_datetime(df_dates['Date_String'])
df_dates['Year'] = df_dates['Date'].dt.year
df_dates['Month'] = df_dates['Date'].dt.month
print("Parsed dates:\n", df_dates)

import matplotlib.pyplot as plt

# Sample data
data = pd.DataFrame({
    'Month': ['Jan', 'Feb', 'Mar', 'Apr'],
    'Sales': [100, 120, 140, 110]
})

# Line plot
data.plot(x='Month', y='Sales', kind='line')
plt.title('Monthly Sales')
plt.show()

# Bar plot
data.plot(x='Month', y='Sales', kind='bar')
plt.show()

# Sales data analysis
sales_data = pd.DataFrame({
    'Date': pd.date_range('2024-01-01', periods=10),
    'Product': ['A', 'B'] * 5,
    'Quantity': [10, 15, 12, 18, 14, 16, 11, 19, 13, 17],
    'Price': [100, 150] * 5
})

sales_data['Revenue'] = sales_data['Quantity'] * sales_data['Price']
summary = sales_data.groupby('Product').agg({
    'Quantity': 'sum',
    'Revenue': 'sum'
})
print("Product summary:\n", summary)

# Customer segmentation
customers = pd.DataFrame({
    'Customer': ['A', 'B', 'C', 'D', 'E'],
    'Age': [25, 35, 45, 30, 40],
    'Spending': [1000, 2000, 1500, 3000, 2500]
})

# Create age groups
customers['Age_Group'] = pd.cut(customers['Age'], 
                               bins=[0, 30, 40, 100], 
                               labels=['Young', 'Middle', 'Senior'])

segment_analysis = customers.groupby('Age_Group')['Spending'].mean()
print("Spending by age group:\n", segment_analysis)