read_table()

import pandas as pd 
my_data = pd.read_table('D:\\student.tsv')
print(my_data)

    id         name class1  mark     sex
0    1     John Deo   Four    75  female
1    2     Max Ruin  Three    85    male
2    3       Arnold  Three    55    male
3    4   Krish Star   Four    60  female
4    5    John Mike   Four    60  female
--------------------------
--------------

import pandas as pd 
my_data = pd.read_csv('D:\\student.csv',sep='\t')
print(my_data)

Example 1: Reading a File with a Custom Delimiter

import pandas as pd
df = pd.read_table("data.txt", sep=";")
print(df)

• This example demonstrates how to read a file where fields are separated by a semicolon (;) instead of the default tab character.
• By specifying the sep parameter, we can correctly parse files with custom delimiters.

Example 2: Skipping Specific Rows While Reading

import pandas as pd
df = pd.read_table("data.txt", skiprows=[0, 2])
print(df)

• This code skips the first and third rows (indices 0 and 2) of the file while reading.
• Useful when certain rows contain metadata or irrelevant information.

Example 3: Reading Only Specific Columns

import pandas as pd
df = pd.read_table("data.txt", usecols=["Name", "Age"])
print(df)

• This example reads only the "Name" and "Age" columns from the file.
• Efficient for large datasets where only specific columns are needed.

Example 4: Handling Missing Values

import pandas as pd
df = pd.read_table("data.txt", na_values=["NA", "?"])
print(df)

• Specifies additional strings to recognize as NaN (missing values), such as "NA" and "?".
• Ensures accurate representation of missing data in the DataFrame.

Example 5: Parsing Dates While Reading

import pandas as pd
df = pd.read_table("data.txt", parse_dates=["Date"])
print(df.dtypes)

• Automatically parses the "Date" column into datetime objects.
• Facilitates time series analysis and date-based operations.

Example 6: Setting a Specific Column as Index

import pandas as pd
df = pd.read_table("data.txt", index_col="ID")
print(df.head())

• Sets the "ID" column as the index of the DataFrame.
• Useful for data alignment and retrieval based on unique identifiers.

Example 7: Reading a File with Encoding Specification

import pandas as pd
df = pd.read_table("data.txt", encoding="utf-8")
print(df)

• Specifies the encoding of the file, which is essential when dealing with non-ASCII characters.
• Prevents encoding-related errors during file reading.

Example 8: Handling Bad Lines in the File

import pandas as pd
df = pd.read_table("data.txt", on_bad_lines="skip")
print(df)

• Skips lines with too many fields (bad lines) instead of raising an error.
• Ensures that the reading process continues smoothly despite malformed lines.

Example 9: Reading a Large File in Chunks

import pandas as pd
chunk_iter = pd.read_table("large_data.txt", chunksize=1000)
for chunk in chunk_iter:
    print(chunk.head())

• Reads the file in chunks of 1000 rows, which is memory-efficient for large files.
• Allows processing of large datasets that cannot fit into memory entirely.

Example 10: Specifying Data Types for Columns

import pandas as pd
dtype_spec = {"ID": str, "Age": int}
df = pd.read_table("data.txt", dtype=dtype_spec)
print(df.dtypes)

• Explicitly sets the data types for specific columns.
• Ensures correct data type assignment, which is crucial for data validation and analysis.

Subhendu Mohapatra

Author & Instructor at plus2net

I write and maintain practical tutorials on Python, PHP, SQL, JavaScript, HTML, jQuery, and web development at plus2net. The tutorials focus on clear explanations, working examples, and code that readers can test and adapt while learning.

About the author   Watch on YouTube

Connect: