Pandas for SQL lovers Reading a CSV file / BULK INSERT

Panda and heart with the word SQLIn this post I will explain how to populate a Pandas DataFrame from a CSV file.

Also in this series:

If you have ever used BULK INSERT to read a CSV file into a database table, you are going to fall in love with the functionality for reading a csv file into a Pandas DataFrame!

Let’s start with how to:

  • Read a file
  • Handle error rows
  • Specify the index column
  • Specify column delimiters
  • Specify column headers
  • Select a subset of rows or columns

We load a csv file into a Pandas dataframe using read_csv


As usual the first thing we need to do is import the numpy and pandas libraries

import pandas as pd
import numpy as np

I have the following csv file:

Sidney ,Crosby,PIT,C,87,8700000,1987-08-07

Use the read_csv method to load a comma separated file into a DataFrame:

players = pd.read_csv('HockeyPlayers.csv')

Creates the following DataFrame:


A few things to note:

  • Column names were read by default from the first row in the file
  • An index column was added numbering each row
  • The Salary for Daniel & Henrik Sedin was blank in the original file and appears as NaN (we will talk more about handling blanks and nulls in a later post)

Handling invalid rows

By default invalid rows will raise an error, for example if my data has rows with too many values.

I have extra ‘,’ in Connor McDavid’s name and in Carey Price salary:

Sidney ,Crosby,PIT,C,87,8700000,1987-08-07

If I try to load the data I get an error:

ParserError: Error tokenizing data. C error: Expected 7 fields in line 3, saw 8

If you specify error_bad_lines=False, invalid rows are skipped:

players = pd.read_csv('HockeyPlayersInvalidRows.csv',error_bad_lines=False)


In Jupyter Notebooks you will see a message informing you lines were skipped:

'Skipping line 3: expected 7 fields, saw 8\nSkipping line 5: expected 7 fields, saw 9

Specifying your own index column

As we saw in part 2 of the series populating a DataFrame, every DataFrame has an index column, but if you do not want an index column created, you can specify a column in your csv file to use as the index column. The index column does not need to be a numeric value.

NOTE: unlike a PRIMARY KEY in a database table, an index column in a DataFrame can contain duplicate values.

players = pd.read_csv('HockeyPlayers.csv', index_col='LastName')

Would give me a DataFrame that uses LastName as the index column:


You can specify a combination of columns to use as your index:

players = pd.read_csv('HockeyPlayers.csv', index_col=['LastName','FirstName'])

Will return the following DataFrame:


Column delimiters

The default column delimiter is a comma. If you use a different delimiter all the columns are read as a single column.

If I try to read a data file using semi-colons as column delimiters

Sidney ;Crosby;PIT;C;87;8700000;1987-08-07

players = pd.read_csv('HockeyPlayersSemiColonDelimiter.csv')

I get the following DataFrame:


Use the delimiter parameter to specify the correct delimiter character

players = pd.read_csv('HockeyPlayersSemiColonDelimiter.csv', delimiter=';')

returns the DataFrame:


Column names

If your file does not contain a row with column names, the first row by default will be treated as column headers, so you might end up with a DataFrame like this:


If you specify header=None, columns will be identified by number:

players = pd.read_csv('HockeyPlayersNoHeaderRow.csv', header=None)


You can specify column names using the names parameter

players = pd.read_csv('HockeyPlayersNoHeaderRow.csv',
names = ['First','Last','TeamCode','Pos','JerseyNbr','Salary','Birthdate'])

Will return the DataFrame:


If your csv file contains column headers you can use the names parameter to rename the columns.

players = pd.read_csv('HockeyPlayers.csv',

names = ['First','Last','TeamCode','Pos','JerseyNbr','Salary','Birthdate'])

But be warned, if the first row of your file contains column names and you specify the names parameter, the first row is read as a row of data:


You need to specify the header parameter and indicate the first row of the file is a header row and not a data row

players = pd.read_csv('HockeyPlayers.csv',
names = ['First','Last','TeamCode','Pos','JerseyNbr','Salary','Birthdate'])

Will give you the desired DataFrame:


Reading a subset of the csv file

You can specify which columns to read from the file with the usecols parameter:

players = pd.read_csv('HockeyPlayers.csv',



Use skiprows to skip rows at the start of the file. Of course if you skip the row containing the column headers, you need to specify the column names as well.

players = pd.read_csv('HockeyPlayers.csv',

will give you the DataFrame:


Use skipfooter to skip rows at the bottom of the file. If you use skipfooter you must also specify the parameter engine=Python. read_csv supports a C, and a Python engine. The C engine is faster, but does not support all the features. The Python engine supports all the features of read_csv.

Now when I use the Python engine, I noticed in Jupyter Notebooks that the first column name gets weird characters appended to it.

players = pd.read_csv('HockeyPlayers.csv',




This might just be something odd with my file, or with my notebook, but I can fix it easily enough by just specifying my own column names and skipping the row containing the column names:

players = pd.read_csv('HockeyPlayers.csv',


If you want to read in a specific number of rows you can use the nrows parameter:

players = pd.read_csv('HockeyPlayers.csv', nrows=3)


If you have a very large data file you can also read it in chunks using the chunksize parameter and store each chunk separately for analysis or processing.

for playerchunk in pd.read_csv('HockeyPlayers.csv', chunksize=2):



Okay we’ve covered how to

    • Read a csv file
    • Control behaviour for error rows
    • Specify an index column
    • Specify column names
    • Specify which rows and columns to load

Still to come we will look at how you work with read_csv to control the column, handle nulls, and date handling. Stay tuned!


6 responses to this post.

  1. […] part 3 of the series I covered how to load a CSV file into a Pandas DataFrame. In this post I’ll focus on how to deal with NULL or missing values read from CSV […]


  2. […] Part 3 How to load a CSV file into a Pandas DataFrame (BULK INSERT) […]


  3. […] How to load a CSV file into a Pandas DataFrame (BULK INSERT) […]


  4. […] How to load a CSV file into a Pandas DataFrame (BULK INSERT) […]


  5. […] How to load a CSV file into a Pandas DataFrame (BULK INSERT) […]


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