Python is a good language for doing data analysis because of the amazing ecosystem of data-centric python packages. pandas package is one of them and makes importing and analyzing data so much easier.
Here, we will discuss how to load a csv file into a Dataframe. It is done using a pandas.read_csv() method. We have to import pandas library to use this method.
Syntax: pd.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, 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, escapechar=None, comment=None, encoding=None, dialect=None, tupleize_cols=None, error_bad_lines=True, warn_bad_lines=True, skipfooter=0, doublequote=True, delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None)
Some Useful parameters are given below :
| Parameter | Use |
|---|---|
| filepath_or_buffer | URL or Dir location of file |
| sep | Stands for separator, default is ‘, ‘ as in csv(comma separated values) |
| index_col | This parameter is use to make passed column as index instead of 0, 1, 2, 3…r |
| header | This parameter is use to make passed row/s[int/int list] as header |
| use_cols | This parameter is Only uses the passed col[string list] to make data frame |
| squeeze | If True and only one column is passed then returns pandas series |
| skiprows | This parameter is use to skip passed rows in new data frame |
| skipfooter | This parameter is use to skip Number of lines at bottom of file |
This method uses comma ‘, ‘ as a default delimiter but we can also use a custom delimiter or a regular expression as a separator.
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Example 1 : Using the read_csv() method with default separator i.e. comma(, )
Python3
# Importing pandas libraryimport pandas as pd# Using the function to load# the data of example.csv# into a Dataframe dfdf = pd.read_csv('example1.csv')# Print the Dataframedf |
Output:
Example 2: Using the read_csv() method with ‘_’ as a custom delimiter.
Python3
# Importing pandas libraryimport pandas as pd# Load the data of example.csv# with '_' as custom delimiter# into a Dataframe dfdf = pd.read_csv('example2.csv', sep = '_', engine = 'python')# Print the Dataframedf |
Output:
Note:While giving a custom specifier we must specify engine=’python’ otherwise we may get a warning like the one given below:
Example 3 : Using the read_csv() method with tab as a custom delimiter.
Python3
# Importing pandas libraryimport pandas as pd# Load the data of example.csv# with tab as custom delimiter# into a Dataframe dfdf = pd.read_csv('example3.csv', sep = '\t', engine = 'python')# Print the Dataframedf |
Output:
Example 4 : Using the read_csv() method with regular expression as custom delimiter.
Let’s suppose we have a csv file with multiple type of delimiters such as given below.
totalbill_tip, sex:smoker, day_time, size
16.99, 1.01:Female|No, Sun, Dinner, 2
10.34, 1.66, Male, No|Sun:Dinner, 3
21.01:3.5_Male, No:Sun, Dinner, 3
23.68, 3.31, Male|No, Sun_Dinner, 2
24.59:3.61, Female_No, Sun, Dinner, 4
25.29, 4.71|Male, No:Sun, Dinner, 4
To load such file into a dataframe we use regular expression as a separator.
Python3
# Importing pandas libraryimport pandas as pd# Load the data of example.csv# with regular expression as# custom delimiter into a# Dataframe dfdf = pd.read_csv('example4.csv', sep = '[:, |_]', engine = 'python')# Print the Dataframedf |
Output:
