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Loading Data into Pandas

Pandas can read data from many file formats with simple functions.

Reading CSV Files

import pandas as pd

# Basic CSV read
df = pd.read_csv('data.csv')

# Common parameters
df = pd.read_csv('data.csv',
    sep=',',           # Delimiter (default comma)
    header=0,          # Row to use as column names
    index_col=0,       # Column to use as index
    usecols=['A','B'], # Only read these columns
    nrows=100,         # Only read first 100 rows
    skiprows=1,        # Skip first row
    na_values=['NA'],  # Values to treat as NaN
    dtype={'col': str} # Specify column types
)

Reading Excel Files

# Read Excel
df = pd.read_excel('data.xlsx')

# Specific sheet
df = pd.read_excel('data.xlsx', sheet_name='Sheet2')

# All sheets (returns dict)
all_sheets = pd.read_excel('data.xlsx', sheet_name=None)

Reading JSON

# JSON file
df = pd.read_json('data.json')

# JSON string
df = pd.read_json('{"name": ["Alice"], "age": [25]}')

# Nested JSON
df = pd.json_normalize(nested_dict)

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Saving Your Data

Writing to CSV

# Basic save
df.to_csv('output.csv')

# Common options
df.to_csv('output.csv',
    index=False,       # Don't save index
    sep=',',           # Delimiter
    columns=['A','B'], # Only these columns
    header=True,       # Include column names
    encoding='utf-8'   # Character encoding
)

Writing to Excel

# Single sheet
df.to_excel('output.xlsx', index=False)

# Multiple sheets
with pd.ExcelWriter('output.xlsx') as writer:
    df1.to_excel(writer, sheet_name='Data')
    df2.to_excel(writer, sheet_name='Summary')

Writing to JSON

# Default orientation
df.to_json('output.json')

# Different orientations
df.to_json('output.json', orient='records')  # List of dicts
df.to_json('output.json', orient='index')    # Dict of dicts

Other Formats

# Parquet (efficient binary format)
df.to_parquet('output.parquet')
df = pd.read_parquet('output.parquet')

# SQL database
from sqlalchemy import create_engine
engine = create_engine('sqlite:///db.sqlite')
df.to_sql('table_name', engine)
df = pd.read_sql('SELECT * FROM table_name', engine)

Reading Data Files

Reading Data Files

Loading Data into Pandas

Reading CSV Files

Reading Excel Files

Reading JSON