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 skip rows while reading csv file. We will use read_csv() method of Pandas library for this task.
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 |
For downloading the student.csv file Click Here
Method 1: Skipping N rows from the starting while reading a csv file.
Code:
Python3
# Importing Pandas libraryimport pandas as pd# Skipping 2 rows from start in csv# and initialize it to a dataframedf = pd.read_csv("students.csv", skiprows = 2)# Show the dataframedf |
Output :
Method 2: Skipping rows at specific positions while reading a csv file.
Code:
Python3
# Importing Pandas libraryimport pandas as pd# Skipping rows at specific positiondf = pd.read_csv("students.csv", skiprows = [0, 2, 5])# Show the dataframedf |
Output :
Method 3: Skipping N rows from the starting except column names while reading a csv file.
Code:
Python3
# Importing Pandas libraryimport pandas as pd# Skipping 2 rows from start# except the column namesdf = pd.read_csv("students.csv", skiprows = [i for i in range(1, 3) ])# Show the dataframedf |
Output :
Method 4: Skip rows based on a condition while reading a csv file.
Code:
Python3
# Importing Pandas libraryimport pandas as pd# function for checking and# skipping every 3rd linedef logic(index): if index % 3 == 0: return True return False# Skipping rows based on a conditiondf = pd.read_csv("students.csv", skiprows = lambda x: logic(x) )# Show the dataframedf |
Output :
Method 5: Skip N rows from the end while reading a csv file.
Code:
Python3
# Importing Pandas libraryimport pandas as pd# Skipping 2 rows from enddf = pd.read_csv("students.csv", skipfooter = 5, engine = 'python')# Show the dataframedf |
Output :
