The_MAK Pro

Pandas CSV

Pandas provides functions for both reading from and writing to CSV files.

CSV stands for Comma-Separated Values. It is a popular file format used for storing tabular data, where each row represents a record, and columns are separated by a delimiter (generally a comma).

For example, contents of a CSV file may look like,

Employee ID,First Name,Last Name,Department,Position,Salary
101,John,Doe,Marketing,Manager,50000
102,Jane,Smith,Sales,Associate,35000
103,Michael,Johnson,Finance,Analyst,45000
104,Emily,Williams,HR,Coordinator,40000

Read CSV Files

In Pandas, the read_csv() function allows us to read data from a CSV file into a DataFrame. It automatically detects commas and parses the data into appropriate columns.

Here’s an example of reading a CSV file using Pandas:

import pandas as pd

# read csv file
df = pd.read_csv('data.csv', header = 0)

print(df)

Output

   Employee ID First Name Last Name Department     Position  Salary
0          101       John       Doe  Marketing      Manager   50000
1          102       Jane     Smith      Sales    Associate   35000
2          103    Michael   Johnson    Finance      Analyst   45000
3          104      Emily  Williams         HR  Coordinator   40000

The above code reads the contents of the data.csv file and creates a DataFrame named df containing the data from the CSV file.

Here, header = 0 sets the first row as the header of the dataframe.

The contents of the data.csv file are the same as the contents of the CSV file provided in the introduction section.

Note: data.csv file should be present in the current directory for the above code to work. If it is in a different directory, you will need to provide the full path to the file.

For example, if the file data.csv is in the folder csv_files, the path './csv_files/data.csv' should be specified as:

df = pd.read_csv('./csv_files/data.csv', header = 0)

read_csv() Syntax

The syntax of read_csv() in Pandas is:

df = pd.read_csv(
    filepath_or_buffer,
    sep=',',
    header=0,
    names=['col1', 'col2', 'col3'],
    index_col='col1',
    usecols=['col1', 'col3'],
    skiprows=[1, 3],
    nrows=100,
    skipinitialspace=True
)

Here,

  • filepath_or_buffer: represents the path or buffer object containing the CSV data to be read
  • sep(optional): specifies the delimiter used in the CSV file
  • header(optional): indicates the row number to be used as the header or column names
  • names(optional): a list of column names to assign to the DataFrame
  • index_col(optional): specifies the column to be used as the index of the DataFrame
  • usecols(optional): a list of columns to be read and included in the DataFrame
  • skiprows(optional): used to skip specific rows while reading the CSV file
  • nrows(optional): sets the maximum number of rows to be read from the CSV file
  • skipinitialspace(optional): determines whether to skip any whitespace after the delimiter in each field

These are some commonly used arguments of the read_csv() function. All of them are optional except filepath_or_buffer. There are many other optional arguments of read_csv().

To learn more, please refer to the official documentation on read_csv().

Example: read_csv() With Arguments

Let’s suppose that we have a CSV file named data.csv with the following contents:

23, 'Hello', 45.6
56, 'World', 78.9
89, 'Foo', 12.3
34, 'Bar', 56.7

Now, let’s load this CSV file into a DataFrame.

import pandas as pd

# read csv file with some arguments
df = pd.read_csv('data.csv', header = None, names = ['col1', 'col2', 'col3'], skiprows = 2)

print(df)

Output

   col1    col2  col3
0    89   'Foo'  12.3
1    34   'Bar'  56.7

In this example, we read a CSV file using the read_csv() method. We specified some arguments while reading the file to load the necessary data in appropriate format.

Here,

  • header = None : indicates that the file doesn’t have a header row
  • names = ['col1', 'col2', 'col3'] : assigns the column names as 'col1', 'col2' and 'col3'
  • skiprows = 2 : skips the first two rows

Write to CSV Files

We used read_csv() to read data from a CSV file into a DataFrame.

Pandas also provides the to_csv() function to write data from a DataFrame into a CSV file.

Let’s see an example.

import pandas as pd

# create a dictionary
data = {'Name': ['John', 'Alice', 'Bob'],
        'Age': [25, 30, 35],
        'City': ['New York', 'London', 'Paris']}

# create a dataframe from the dictionary
df = pd.DataFrame(data)

# write dataframe to csv file
df.to_csv('output.csv', index=False)

Output

Name,Age,City
John,25,New York
Alice,30,London
Bob,35,Paris

Here, the above code writes the DataFrame df to the output.csv file. The index=False parameter is used to exclude the index labels from the CSV file.

Note: The above code will create a new file named output.csv in the current directory (unless a different directory is specified in the file path).

If the file output.csv already exists in the current directory, running this code will overwrite the existing file with the new contents of the DataFrame.

to_csv() Syntax

The syntax of to_csv() in Pandas is:

df.to_csv(
    path_or_buf,
    sep=',',
    header=True,
    index=False,
    mode='w',
    encoding=None,
    quoting=None,
    line_terminator='\n',
)

Here,

  • path_or_buf: represents the path or buffer object where the DataFrame will be saved as a CSV file
  • sep(optional): specifies the delimiter to be used in the output CSV file
  • header(optional): indicates whether to include the header row in the output CSV file
  • index(optional): determines whether to include the index column in the output CSV file
  • mode(optional): specifies the mode in which the output file will be opened
  • encoding(optional): sets the character encoding to be used when writing the CSV file
  • quoting(optional): determines the quoting behavior for fields that contain special characters
  • line_terminator(optional): specifies the character sequence used to terminate lines in the output CSV file

These are some commonly used arguments of the to_csv() function. All of them are optional except path_or_buf. There are many other optional arguments that can be used with to_csv().

To learn more, please refer to the official documentation to_csv().

Example: to_csv() With Arguments

import pandas as pd

# create dataframe
data = {'Name': ['Tom', 'Nick', 'John', 'Tom'],
        'Age': [20, 21, 19, 18],
        'City': ['New York', 'London', 'Paris', 'Berlin']}
df = pd.DataFrame(data)

# write to csv file
df.to_csv('output.csv', sep = ';', index = False, header = True)

Output

Name;Age;City
Tom;20;New York
Nick;21;London
John;19;Paris
Tom;18;Berlin

In this example, we wrote a DataFrame to the CSV file 'output.csv' using the to_csv() method. We used some arguments to write necessary data to the file in required format.

Here,

  • sep = ';' : specifies the delimiter as ';'
  • index = False : instructs not to include the index column in the output file
  • header = True : instructs to include the column names as the header in the output file
© 2024 All rights reserved. | Made With 🤍 By The_MAK Team