Pandas.read_csv() Method

Pandas read_csv() function provides a straightforward way to read data from a CSV file and convert it into a DataFrame.

Syntax

pd.read_csv(filepath_or_buffer, sep=’ ,’ , header=’infer’, 
            index_col=None, usecols=None, engine=None, 
            skiprows=None, nrows=None) 

Parameters

1. sep or delimiter: Specifies the delimiter used to separate fields in the CSV file (default is ,).
   
2. header: Indicates the row number(s) to use as the column names (default is 0, meaning the first row is used as column names). If there are no column names in the file, set header=None.
   
3. index_col: Specifies the column(s) to use as the index (row labels) of the DataFrame.
   
4. names: Specifies a list of column names to use when header=None.
   
5. skiprows: Skips a specified number of rows or a list of row indices to skip while reading the file.
   
6. na_values: Specifies additional strings to recognize as NaN (Not a Number) or missing values.
   
7. dtype: Specifies the data type for one or more columns, provided as a dictionary.

Example 1: Read CSV File using Pandas read_csv

For this example, we will use the mlb_players.csv file.

To import this csv file and convert it into a DataFrame, you must use the “pandas.read_csv()” function.

# Import pandas
import pandas as pd
 
# reading csv file 
df = pd.read_csv("mlb_players.csv")

print(df)

Output

Example 2: Using ‘usecols’ in read_csv()

In this code, we are specifying only 3 columns,i.e.[‘Name’, ‘Position’, ‘Age’], to load, and we use the header 0 as its default header.

# Import pandas
import pandas as pd

# reading csv file
df = pd.read_csv("mlb_players.csv",
      header=0,
      usecols=['Name', 'Position', 'Age']
)

df.head()

Output

Example 3: Using index_col in read_csv()

# Import pandas
import pandas as pd

# reading csv file
df = pd.read_csv("mlb_players.csv",
      header=0,
      usecols=['Name', 'Position', 'Age'],
      index_col=['Name', 'Age'],
 )

df.head()  

Output

Example 4: Using nrows in read_csv()

We can just display only 4 rows using the nrows parameter.

# Import pandas
import pandas as pd

# reading csv file
df = pd.read_csv("mlb_players.csv",
     header=0,
     usecols=['Name', 'Position', 'Age'],
     index_col=['Name', 'Age'],
     nrows=4
 )

df

Output

Example 5: Using skiprows in read_csv()

Before skipping the rows

# Import pandas
import pandas as pd

# reading csv file
df = pd.read_csv("mlb_players.csv", header = 0)

df

After skipping the rows

# Import pandas
import pandas as pd

# reading csv file
df = pd.read_csv("mlb_players.csv",
      header=0,
      skiprows=[1, 3]
 )

df

Output

That’s it.

Krunal

Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Python language stands as a testament to his versatility and commitment to the craft.