
Overview
The pandas.read_csv()
function is a powerful utility in the Pandas library that allows you to read data from a CSV (Comma-Separated Values) file into a Pandas DataFrame. This function is widely used in data analysis to load datasets for processing and manipulation. It simplifies the process of converting raw CSV data into a structured format that can be easily analyzed and manipulated using Pandas’ various tools and functions.
Syntax
The basic syntax of the pandas.read_csv()
function is as follows:
pandas.read_csv(filepath_or_buffer, sep=',', delimiter=None, header='infer', names=None,
skiprows=None, skipfooter=0, na_values=None,
parse_dates=False, dtype=None,
engine='c', encoding='utf-8',
compression='infer',
thousands=None,
decimal='.',
lineterminator=None,
quotechar='"',
quoting=0,
doublequote=True,
escapechar=None,
comment=None,
parse_dates=False,
error_bad_lines=None,
warn_bad_lines=None,
skip_blank_lines=True,
chunksize=None,
skipinitialspace=False,
na_filter=True,
keep_default_na=True,
convert_float=True,
verbose=False,
memory_map=False,
low_memory=True,
float_precision=None)
Parameters
filepath_or_buffer
: The name of the CSV file or a file-like object.sep
: The delimiter that separates the values (default is a comma).header
: Row number(s) to use as the column names; defaults to ‘infer’.names
: List of column names to use if there is no header row.skiprows
: Number of rows to skip at the start of the file.na_values
: Additional strings to recognize as NA/NaN.parse_dates
: Parsing dates to datetime format.encoding
: Encoding to use for reading the file (e.g., ‘utf-8’).
Example
Let’s consider a scenario where we have a CSV file named employee_sales.csv
containing sales data. The file has the following structure:
Employee,Product,Month,Sales
Alice,Widget A,2023-01,200
Bob,Widget B,2023-01,150
Alice,Widget B,2023-02,300
Bob,Widget A,2023-02,200
We want to load this data into a Pandas DataFrame and analyze the sales performance of each employee.
We can use the pandas.read_csv()
function like this:
import pandas as pd
# Load the CSV file into a DataFrame
df = pd.read_csv('employee_sales.csv')
# Display the DataFrame
print(df)
The resulting DataFrame will look like this:
Employee Product Month Sales
0 Alice Widget A 2023-01 200
1 Bob Widget B 2023-01 150
2 Alice Widget B 2023-02 300
3 Bob Widget A 2023-02 200
Now we have the sales data structured in a DataFrame format, which allows us to perform further analysis, such as grouping sales by employee or product, calculating total sales, or visualizing the data.