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.