Reading Different File Formats with Pandas: CSV, Excel, JSON

Learn to read CSV, Excel, JSON, and other file formats with pandas. Master data loading, parsing options, and handling common issues. Complete guide with practical examples.

By Techietory on February 14, 2026
Reading Different File Formats with Pandas: CSV, Excel, JSON

Introduction

After learning pandas fundamentals including DataFrames and basic operations, you face a practical question: how do you get real-world data into pandas for analysis? While creating DataFrames manually from Python dictionaries or lists works for learning and small datasets, real data science projects work with data stored in files: CSV files exported from spreadsheets or databases, Excel workbooks shared by colleagues, JSON files from web APIs, and dozens of other formats. Pandas provides robust functions for reading these formats, intelligently handling data types, missing values, encoding issues, and structural variations that make manual parsing tedious and error-prone.

The ability to load data from various sources represents a fundamental data science skill. You might receive customer data as CSV, sales figures in Excel, API responses as JSON, and sensor readings as Parquet. Each format has conventions, quirks, and edge cases that pandas handles through flexible reading functions. Understanding these functions thoroughly prevents hours of frustration fighting with data loading issues and enables you to start analyzing data immediately rather than spending time on custom parsers. Moreover, pandas’ reading functions provide parameters for handling virtually any variation you encounter: different delimiters, custom missing value indicators, date parsing, column selection, and more.

What makes pandas’ I/O capabilities particularly valuable is their consistency across formats. While CSV, Excel, and JSON differ substantially in structure and complexity, pandas provides similar interfaces for reading them, always returning DataFrames ready for analysis. This consistency means that once you learn the patterns for one format, you can adapt quickly to others. The functions also handle common data quality issues automatically: converting numeric strings to numbers, parsing dates intelligently, and detecting missing values. This intelligent default behavior saves enormous time compared to lower-level file reading approaches.

This comprehensive guide explores reading the most common data formats you will encounter in data science. You will learn how to read CSV files with various delimiters and options, how to load Excel workbooks including specific sheets and ranges, how to parse JSON data from files and strings, how to handle common issues like encoding problems and messy headers, and best practices for efficient data loading. You will also discover how to specify data types, parse dates, handle missing values, and select subsets during loading to work efficiently with large files. By the end, you will confidently load data from any common format and troubleshoot issues that arise.

Reading CSV Files: The Most Common Format

CSV (Comma-Separated Values) represents the most universal data exchange format. Pandas provides read_csv() for loading CSV files, with extensive options for handling variations.

Read a basic CSV file:

Python
import pandas as pd

# Read CSV file
df = pd.read_csv('data.csv')
print(df.head())

This simplest form works when files have:

  • Comma delimiters
  • Headers in the first row
  • Standard encoding (UTF-8)
  • No problematic missing values

Specify delimiter for non-comma files:

Python
# Tab-separated values
df = pd.read_csv('data.tsv', delimiter='\t')

# Pipe-delimited
df = pd.read_csv('data.txt', delimiter='|')

# Arbitrary delimiter
df = pd.read_csv('data.txt', sep=';')

The sep parameter (alias for delimiter) accepts any string or regex pattern.

Handle files without headers:

Python
# File has no header row
df = pd.read_csv('data.csv', header=None)
print(df.head())

This creates default column names: 0, 1, 2, etc.

Specify custom column names:

Python
# Provide column names
df = pd.read_csv('data.csv', 
                 header=None,
                 names=['Name', 'Age', 'City', 'Salary'])

Skip rows at the beginning:

Python
# Skip first 3 rows (common for files with metadata at top)
df = pd.read_csv('data.csv', skiprows=3)

# Skip specific rows by number
df = pd.read_csv('data.csv', skiprows=[0, 2, 5])

# Skip rows based on condition
df = pd.read_csv('data.csv', skiprows=lambda x: x < 3 or x % 2 == 0)

Read only specific columns:

Python
# Select columns by name
df = pd.read_csv('data.csv', usecols=['Name', 'Age', 'Salary'])

# Select columns by position
df = pd.read_csv('data.csv', usecols=[0, 1, 3])

# Select using function
df = pd.read_csv('data.csv', usecols=lambda col: col.startswith('sales'))

This saves memory when files have many columns you don’t need.

Specify data types explicitly:

Python
# Define data types for columns
dtypes = {
    'Name': str,
    'Age': int,
    'Salary': float,
    'City': 'category'  # Memory-efficient for repeated values
}

df = pd.read_csv('data.csv', dtype=dtypes)

Explicit types prevent pandas from inferring incorrectly and can save memory.

Parse dates automatically:

Python
# Parse single date column
df = pd.read_csv('data.csv', parse_dates=['Date'])

# Parse multiple date columns
df = pd.read_csv('data.csv', parse_dates=['Start_Date', 'End_Date'])

# Combine columns into date
df = pd.read_csv('data.csv', 
                 parse_dates={'Date': ['Year', 'Month', 'Day']})

Handle missing values:

Python
# Recognize additional missing value indicators
df = pd.read_csv('data.csv', 
                 na_values=['NA', 'N/A', 'missing', '?', '-'])

# Different missing values per column
na_dict = {
    'Age': ['unknown', '?'],
    'Salary': ['-', 'N/A', 0]
}
df = pd.read_csv('data.csv', na_values=na_dict)

Handle encoding issues:

Python
# Specify encoding explicitly
df = pd.read_csv('data.csv', encoding='utf-8')

# Common encodings
df = pd.read_csv('data.csv', encoding='latin-1')  # Western European
df = pd.read_csv('data.csv', encoding='cp1252')   # Windows
df = pd.read_csv('data.csv', encoding='iso-8859-1')

# Handle encoding errors
df = pd.read_csv('data.csv', encoding='utf-8', encoding_errors='replace')

Read compressed files directly:

Python
# Pandas automatically detects and decompresses
df = pd.read_csv('data.csv.gz')
df = pd.read_csv('data.csv.zip')
df = pd.read_csv('data.csv.bz2')

Read from URL:

Python
url = 'https://example.com/data.csv'
df = pd.read_csv(url)

Read only first N rows:

Python
# Load just 1000 rows (useful for previewing large files)
df = pd.read_csv('data.csv', nrows=1000)

Set index while reading:

Python
# Use first column as index
df = pd.read_csv('data.csv', index_col=0)

# Use specific column as index
df = pd.read_csv('data.csv', index_col='ID')

# Multi-level index
df = pd.read_csv('data.csv', index_col=['Country', 'Year'])

Reading Excel Files: Working with Workbooks

Excel files (.xlsx, .xls) require the openpyxl or xlrd library for reading. Install with pip install openpyxl.

Read Excel file:

Python
# Read first sheet
df = pd.read_excel('data.xlsx')
print(df.head())

Specify which sheet to read:

Python
# By sheet name
df = pd.read_excel('data.xlsx', sheet_name='Sales')

# By position (0-indexed)
df = pd.read_excel('data.xlsx', sheet_name=0)

# Read multiple sheets (returns dictionary)
dfs = pd.read_excel('data.xlsx', sheet_name=['Sales', 'Inventory'])
print(dfs['Sales'].head())

# Read all sheets
dfs = pd.read_excel('data.xlsx', sheet_name=None)
for sheet_name, df in dfs.items():
    print(f"Sheet: {sheet_name}")
    print(df.head())

Specify range to read:

Python
# Read specific rows
df = pd.read_excel('data.xlsx', skiprows=2, nrows=100)

# Read specific columns by name
df = pd.read_excel('data.xlsx', usecols='A:D')

# Read specific columns by name
df = pd.read_excel('data.xlsx', usecols=['Name', 'Age', 'City'])

Handle Excel-specific options:

Python
# Specify header row
df = pd.read_excel('data.xlsx', header=2)  # Use row 2 as header

# No header
df = pd.read_excel('data.xlsx', header=None)

# Multi-level header
df = pd.read_excel('data.xlsx', header=[0, 1])

Excel files often have formatting:

Python
# Parse dates from Excel serial dates
df = pd.read_excel('data.xlsx', parse_dates=['Date'])

# Specify data types
df = pd.read_excel('data.xlsx', 
                   dtype={'ID': str, 'Amount': float})

Read from multiple files:

Python
import glob

# Read all Excel files in directory
files = glob.glob('data/*.xlsx')
dfs = [pd.read_excel(f) for f in files]
combined = pd.concat(dfs, ignore_index=True)

Reading JSON Files: Structured Data

JSON (JavaScript Object Notation) stores structured, hierarchical data. Pandas can read JSON files and strings.

Read JSON file:

Python
# Read JSON file
df = pd.read_json('data.json')
print(df.head())

JSON structure affects how pandas interprets it. Common formats:

Records format (list of objects):

Python
[
  {"Name": "Alice", "Age": 25, "City": "Boston"},
  {"Name": "Bob", "Age": 30, "City": "Seattle"}
]

Read records format:

Python
df = pd.read_json('data.json', orient='records')

Split format (columns as keys):

Python
{
  "Name": {"0": "Alice", "1": "Bob"},
  "Age": {"0": 25, "1": 30},
  "City": {"0": "Boston", "1": "Seattle"}
}

Read split format:

Python
df = pd.read_json('data.json', orient='split')

Index format (rows as keys):

Python
{
  "0": {"Name": "Alice", "Age": 25},
  "1": {"Name": "Bob", "Age": 30}
}

Read index format:

Python
df = pd.read_json('data.json', orient='index')

Read JSON from string:

Python
json_string = '''
[
  {"Name": "Alice", "Age": 25},
  {"Name": "Bob", "Age": 30}
]
'''

df = pd.read_json(json_string)

Read nested JSON:

Python
# Flatten nested structures
from pandas import json_normalize

data = [
    {
        "name": "Alice",
        "address": {"city": "Boston", "state": "MA"}
    },
    {
        "name": "Bob", 
        "address": {"city": "Seattle", "state": "WA"}
    }
]

df = json_normalize(data)
print(df)
# Output:
#     name address.city address.state
# 0  Alice       Boston            MA
# 1    Bob      Seattle            WA

Read JSON Lines format (one JSON object per line):

Python
df = pd.read_json('data.jsonl', lines=True)

Common with streaming data and large datasets.

Reading Other Common Formats

Pandas supports many additional formats:

HTML Tables

Extract tables from HTML:

Python
# Read all tables from HTML file or URL
dfs = pd.read_html('tables.html')

# Returns list of DataFrames, one per table
df = dfs[0]  # First table

# Read from URL
url = 'https://example.com/data-table.html'
dfs = pd.read_html(url)

SQL Databases

Read from SQL database:

Python
import sqlite3

# Connect to database
conn = sqlite3.connect('database.db')

# Read entire table
df = pd.read_sql('SELECT * FROM sales', conn)

# Read with query
df = pd.read_sql('SELECT * FROM sales WHERE year = 2024', conn)

# Read table directly
df = pd.read_sql_table('sales', conn)

Parquet Files

Parquet is a columnar storage format, efficient for large datasets:

Python
# Requires pyarrow or fastparquet
# pip install pyarrow

df = pd.read_parquet('data.parquet')

# Read specific columns
df = pd.read_parquet('data.parquet', columns=['Name', 'Age'])

Clipboard

Read data from clipboard:

Python
# Copy data from Excel or browser, then:
df = pd.read_clipboard()

Useful for quick data imports during interactive analysis.

Handling Common Issues

Large Files

For files too large for memory:

Python
# Read in chunks
chunk_size = 10000
chunks = []

for chunk in pd.read_csv('large_file.csv', chunksize=chunk_size):
    # Process chunk
    processed = chunk[chunk['value'] > 0]
    chunks.append(processed)

df = pd.concat(chunks, ignore_index=True)

Or read with iterator:

Python
# Create iterator
reader = pd.read_csv('large_file.csv', iterator=True)

# Read first chunk
chunk = reader.get_chunk(1000)

Malformed Files

Handle files with inconsistent columns:

Python
# Skip bad lines
df = pd.read_csv('messy_data.csv', on_bad_lines='skip')

# Or warn
df = pd.read_csv('messy_data.csv', on_bad_lines='warn')

Handle extra whitespace:

Python
# Strip whitespace from strings
df = pd.read_csv('data.csv', skipinitialspace=True)

Quote Characters

Handle quoted values:

Python
# Values are quoted
df = pd.read_csv('data.csv', quotechar='"')

# Different quote character
df = pd.read_csv('data.csv', quotechar="'")

# No quoting
df = pd.read_csv('data.csv', quoting=3)  # QUOTE_NONE

Decimal Separators

Handle European number format (comma as decimal):

Python
# Comma as decimal separator
df = pd.read_csv('data.csv', decimal=',')

Writing Data: Saving DataFrames

After processing, save results:

Write CSV

Python
# Write to CSV
df.to_csv('output.csv', index=False)

# With specific options
df.to_csv('output.csv', 
          sep=';',
          encoding='utf-8',
          index=False,
          columns=['Name', 'Age'])

Write Excel

Python
# Write to Excel
df.to_excel('output.xlsx', index=False)

# Multiple sheets
with pd.ExcelWriter('output.xlsx') as writer:
    df1.to_excel(writer, sheet_name='Sales', index=False)
    df2.to_excel(writer, sheet_name='Inventory', index=False)

Write JSON

Python
# Write to JSON
df.to_json('output.json', orient='records', indent=2)

# Different orientations
df.to_json('output.json', orient='split')
df.to_json('output.json', orient='index')

Write Parquet

Python
# Write to Parquet
df.to_parquet('output.parquet', index=False)

# With compression
df.to_parquet('output.parquet', compression='gzip')

Best Practices for Data Loading

Follow these patterns for efficient data loading:

Test with small samples first:

Python
# Preview file structure
df_sample = pd.read_csv('large_file.csv', nrows=5)
print(df_sample.head())
print(df_sample.dtypes)

# Then read full file with correct settings
df = pd.read_csv('large_file.csv', 
                 dtype={'ID': str, 'Amount': float},
                 parse_dates=['Date'])

Specify data types when possible:

Python
# Saves memory and prevents misinterpretation
df = pd.read_csv('data.csv', 
                 dtype={'ID': str, 
                        'Category': 'category',
                        'Value': float})

Handle missing values explicitly:

Python
# Be explicit about what represents missing
df = pd.read_csv('data.csv', 
                 na_values=['NA', 'N/A', 'null', '-', '?'])

Use compression for storage:

Python
# Save compressed
df.to_csv('data.csv.gz', compression='gzip', index=False)

# Read compressed (automatic)
df = pd.read_csv('data.csv.gz')

Document data loading:

Python
def load_sales_data(filepath):
    """
    Load sales data from CSV file.
    
    Parameters
    ----------
    filepath : str
        Path to CSV file
        
    Returns
    -------
    DataFrame
        Cleaned sales data
    """
    df = pd.read_csv(
        filepath,
        parse_dates=['Date'],
        dtype={'ProductID': str, 'Quantity': int},
        na_values=['NA', '-']
    )
    return df

Troubleshooting Common Errors

UnicodeDecodeError:

Python
# Try different encodings
for encoding in ['utf-8', 'latin-1', 'cp1252', 'iso-8859-1']:
    try:
        df = pd.read_csv('data.csv', encoding=encoding)
        print(f"Success with {encoding}")
        break
    except UnicodeDecodeError:
        continue

FileNotFoundError:

Python
import os

# Check if file exists
if os.path.exists('data.csv'):
    df = pd.read_csv('data.csv')
else:
    print("File not found")

# Use absolute path
df = pd.read_csv('/full/path/to/data.csv')

ParserError (inconsistent columns):

Python
# Skip bad lines
df = pd.read_csv('data.csv', on_bad_lines='skip')

# Or inspect problematic rows
with open('data.csv') as f:
    for i, line in enumerate(f):
        if line.count(',') != expected_columns:
            print(f"Line {i}: {line}")

MemoryError:

Python
# Read in chunks
chunks = []
for chunk in pd.read_csv('large.csv', chunksize=10000):
    # Process and reduce size
    processed = chunk[chunk['important_col'].notna()]
    chunks.append(processed)

df = pd.concat(chunks, ignore_index=True)

Conclusion

Reading data from files represents the first step in virtually every data science project, making proficiency with pandas’ I/O functions essential. Understanding how to load CSV, Excel, JSON, and other formats with appropriate options for handling variations, missing values, encodings, and data types enables you to start analyzing data immediately rather than struggling with parsing issues. The flexibility pandas provides through parameters like dtype, parse_dates, usecols, and encoding options handles virtually any file format variation you encounter.

The patterns covered here, specifying data types, parsing dates, handling missing values, reading subsets, and managing large files, apply across all formats pandas supports. While specific parameter names might vary between read_csv(), read_excel(), and read_json(), the underlying concepts remain consistent. This consistency makes learning new formats easy once you master the fundamental patterns with common formats.

As you work with real datasets, you will encounter countless variations in how files are structured and formatted. Rather than fighting with manual parsing, leverage pandas’ extensive options to handle these variations cleanly. The time invested learning these reading functions thoroughly pays dividends in every project by eliminating data loading as a source of frustration and enabling you to focus on analysis rather than parsing. Practice loading different file types, experiment with various options, and build intuition for troubleshooting common issues. With solid data loading skills, you can confidently tackle any dataset regardless of its source format.

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