How to use pandas read_csv function || Python read_csv pandas || pd.read_csv In 5 Min.

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Python Tutorial - How to load CSV file into Pandas Data frame.

Welcome back to another exciting tutorial on “How to load CSV file into Pandas Data frame”. In this Python tutorial, you’ll learn the pandas read_csv method. The method read and load the CSV data into Pandas Dataframe.

You’ll also learn various optional and mandatory parameters of the pandas read_csv method syntax. In the end, you will see the live coding demo for better understanding. Let’s begin our tutorial with an introduction to the CSV file, followed by an introduction to Python Pandas and Pandas Dataframe.

Introduction to CSV file.

CSV stands for a Comma-separated (CSV) file. It’s a text file in which each field value is delimited by “,” (comma). These files are generally used to store data into a tabular format.

Pandas Dataframe.

A pandas data frame is an object, that represents data in the form of rows and columns. Python data frames are like excel worksheets or a DB2 table. A pandas data frame has an index row and a header column along with data rows.

Pandas Read_CSV Syntax:
# Python read_csv pandas syntax with
# minimum set of parametrs.
pd.read_csv(filepath,
sep=',',
dtype=None,
header=None,
skiprows=None,
index_col=None,
skip_blank_lines=True,
na_filter=True)

Now, let’s understand the importance of these parameters.

filepath: The filepath parameter specifies the file location. A local file could be passed as://localhost/path/to/table.csv.
sep: The sep parameter specifies the delimiter which is used in the file.
dtype: The dtype parameter specifies the column datatype (i.e. integer or float).
header: The header parameter specifies the column header row. A list of values can be used while reading a CSV file.
skiprows: The skiprows parameter use to skip initial rows, for example, skiprows=05 means data would be read from 06th row.
index_col: The index_col parameter use to specify the column as the row labels of the data frame.
skip_blank_lines: The parameter is used to skip blank lines while reading data from the dataset using read_csv pandas.
na_filter: The parameter is used to drop NaN value from the dataset.
low_memory: Internally process the file in chunks, resulting in lower memory use while parsing, but possibly mixed type inference.
encoding: Encoding to use for UTF when reading/writing (ex. ‘utf-8’).

Python Read_CSV Pandas Example.

import numpy as np # import numpy as np
import pandas as pd # import pandas as pd
# Reading and load loan file into df.
df_loan = pd.read_csv("loan.csv", sep=",",
encoding = "ISO-8859-1",
index_col=None,
low_memory=False,
dtype={'id':np.int32}, nrows=16, skiprows=0)
df_loan.head(3)

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Full Syntax:

# Python read_csv pandas all parameters list.
read_csv(filepath_or_buffer,
sep=',',
delimiter=None,
header='infer',
names=None,
index_col=None,
usecols=None,
squeeze=False,
prefix=None,
mangle_dupe_cols=True,
dtype=None,
engine=None,
converters=None,
true_values=None,
false_values=None,
skipinitialspace=False,
skiprows=None,
skipfooter=0,
nrows=None,
na_values=None,
keep_default_na=True,
na_filter=True,
verbose=False,
skip_blank_lines=True,
parse_dates=False,
infer_datetime_format=False,
keep_date_col=False,
date_parser=None,
dayfirst=False,
iterator=False,
chunksize=None,
compression='infer',
thousands=None,
decimal=b'.',
lineterminator=None,
quotechar='"',
quoting=0,
doublequote=True,
escapechar=None,
comment=None,
encoding=None,
dialect=None,
tupleize_cols=None,
error_bad_lines=True,
warn_bad_lines=True,
delim_whitespace=False,
low_memory=True,
memory_map=False,
float_precision=None)

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