How to load a TSV file into a Pandas Dataframe?

A TSV (Tab Separated Values) file is a text format where data columns are separated by tabs. Pandas provides two main methods to load TSV files into DataFrames: read_table() with delimiter='\t' and read_csv() with sep='\t'.

Method 1: Using read_table() with delimiter='\t'

The read_table() function is specifically designed for reading delimited text files ?

import pandas as pd

# Create a sample TSV data for demonstration
tsv_data = """Name	Age	City	Salary
John	25	New York	50000
Alice	30	London	60000
Bob	28	Paris	55000
Carol	32	Tokyo	65000"""

# Save sample data to a TSV file
with open('sample.tsv', 'w') as f:
    f.write(tsv_data)

# Load TSV file using read_table
df1 = pd.read_table('sample.tsv', delimiter='\t')
print(df1)

    Name  Age      City  Salary
0   John   25  New York   50000
1  Alice   30    London   60000
2    Bob   28     Paris   55000
3  Carol   32     Tokyo   65000

Using Additional Parameters

You can customize the loading with additional parameters ?

# Load with specific index column
df2 = pd.read_table('sample.tsv', delimiter='\t', index_col=0)
print("With index column:")
print(df2)

print("\nWith selected columns:")
# Load only specific columns
df3 = pd.read_table('sample.tsv', delimiter='\t', usecols=['Name', 'Salary'])
print(df3)

With index column:
       Age      City  Salary
Name                        
John    25  New York   50000
Alice   30    London   60000
Bob     28     Paris   55000
Carol   32     Tokyo   65000

With selected columns:
    Name  Salary
0   John   50000
1  Alice   60000
2    Bob   55000
3  Carol   65000

Method 2: Using read_csv() with sep='\t'

The read_csv() function can also handle TSV files by specifying the separator ?

# Load TSV file using read_csv
df4 = pd.read_csv('sample.tsv', sep='\t')
print(df4)

# Get dataframe shape
print(f"\nDataFrame shape: {df4.shape}")

# Show column names
print(f"Columns: {list(df4.columns)}")

    Name  Age      City  Salary
0   John   25  New York   50000
1  Alice   30    London   60000
2    Bob   28     Paris   55000
3  Carol   32     Tokyo   65000

DataFrame shape: (4, 4)
Columns: ['Name', 'Age', 'City', 'Salary']

Handling Large TSV Files

For large files, you can skip rows or load data in chunks ?

# Skip the first row (useful for files with metadata)
df5 = pd.read_table('sample.tsv', delimiter='\t', skiprows=1)
print("After skipping first row:")
print(df5)

# Load only first 2 rows
df6 = pd.read_table('sample.tsv', delimiter='\t', nrows=2)
print("\nFirst 2 rows only:")
print(df6)

After skipping first row:
      John  25  New York  50000
0    Alice  30    London  60000
1      Bob  28     Paris  55000
2    Carol  32     Tokyo  65000

First 2 rows only:
    Name  Age      City  Salary
0   John   25  New York   50000
1  Alice   30    London   60000

Comparison

Common Parameters

• delimiter or sep Specify the field separator

• index_col Set a column as the DataFrame index

• usecols Load only specific columns

• skiprows Skip specified number of rows

• nrows Load only first n rows

Conclusion

Both read_table() and read_csv() can load TSV files effectively. Use read_table() for tab-delimited files by default, or read_csv() with sep='\t' for more flexibility with various delimited formats.