Read CSV files using Pandas – With Examples

The CSV (Comma Separated Values) format is quite popular for storing data. A large number of datasets are present as CSV files which can be used either directly in a spreadsheet software like Excel or can be loaded up in programming languages like R or Python. Pandas dataframes are quite powerful for handling two-dimensional tabular data. In this tutorial, we’ll look at how to read a csv file as a pandas dataframe in python.

The pandas read_csv() function is used to read a CSV file into a dataframe. It comes with a number of different parameters to customize how you’d like to read the file. The following is the general syntax for loading a csv file to a dataframe:

import pandas as pd
df = pd.read_csv(path_to_file)

Here, path_to_file is the path to the CSV file you want to load. It can be any valid string path or a URL (see the examples below). It returns a pandas dataframe. Let’s look at some of the different use-cases of the read_csv() function through examples –

Before we proceed, let’s get a sample CSV file that we’d be using throughout this tutorial. We’ll be using the Iris dataset which you can download from Kaggle. Here’s a snapshot of how it looks when opened in excel:

 Iris dataset snapshot in Excel

To read a CSV file locally stored on your machine pass the path to the file to the read_csv() function. You can pass a relative path, that is, the path with respect to your current working directory or you can pass an absolute path.

# read csv using relative path
import pandas as pd
df = pd.read_csv('Iris.csv')
print(df.head())

Output:

   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species
0   1            5.1           3.5            1.4           0.2  Iris-setosa
1   2            4.9           3.0            1.4           0.2  Iris-setosa
2   3            4.7           3.2            1.3           0.2  Iris-setosa
3   4            4.6           3.1            1.5           0.2  Iris-setosa
4   5            5.0           3.6            1.4           0.2  Iris-setosa

In the above example, the CSV file Iris.csv is loaded from its location using a relative path. Here, the file is present in the current working directory. You can also read a CSV file from its absolute path. See the example below:

# read csv using absolute path
import pandas as pd
df = pd.read_csv(r"C:\Users\piyush\Downloads\Iris.csv")
print(df.head())

Output:

   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species
0   1            5.1           3.5            1.4           0.2  Iris-setosa
1   2            4.9           3.0            1.4           0.2  Iris-setosa
2   3            4.7           3.2            1.3           0.2  Iris-setosa
3   4            4.6           3.1            1.5           0.2  Iris-setosa
4   5            5.0           3.6            1.4           0.2  Iris-setosa

Here, the same CSV file is read from its absolute path.

You can also read a CSV file from its URL. Pass the URL to the read_csv() function and it’ll read the corresponding file to a dataframe. The Iris dataset can also be downloaded from the UCI Machine Learning Repository. Let’s use their dataset download URL to read it as a dataframe.

import pandas as pd
df = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data")
df.head()

Output:

   5.1  3.5  1.4  0.2  Iris-setosa
0  4.9  3.0  1.4  0.2  Iris-setosa
1  4.7  3.2  1.3  0.2  Iris-setosa
2  4.6  3.1  1.5  0.2  Iris-setosa
3  5.0  3.6  1.4  0.2  Iris-setosa
4  5.4  3.9  1.7  0.4  Iris-setosa

You can see that the read_csv() function is able to read a dataset from its URL. It is interesting to note that in this particular data source, we do not have headers. The read_csv() function infers the header by default and here uses the first row of the dataset as the header.

In the above example, you saw that if the dataset does not have a header, the read_csv() function infers it by itself and uses the first row of the dataset as the header. You can change this behavior through the header parameter, pass None if your dataset does not have a header. You can also pass a custom list of integers as a header.

import pandas as pd
df = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data", header=None)
df.head()

Output:

     0    1    2    3            4
0  5.1  3.5  1.4  0.2  Iris-setosa
1  4.9  3.0  1.4  0.2  Iris-setosa
2  4.7  3.2  1.3  0.2  Iris-setosa
3  4.6  3.1  1.5  0.2  Iris-setosa
4  5.0  3.6  1.4  0.2  Iris-setosa

In the above example, we pass header=None to the read_csv() function since the dataset did not have a header.

You can give custom column names to your dataframe when reading a CSV file using the read_csv() function. Pass your custom column names as a list to the names parameter.

import pandas as pd
df = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data",
                 names = ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'])
print(df.head())

Output:

   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species
0            5.1           3.5            1.4           0.2  Iris-setosa
1            4.9           3.0            1.4           0.2  Iris-setosa
2            4.7           3.2            1.3           0.2  Iris-setosa
3            4.6           3.1            1.5           0.2  Iris-setosa
4            5.0           3.6            1.4           0.2  Iris-setosa

You can also use a column as the row labels of the dataframe. Pass the column name to the index_col parameter. Going back to the Iris.csv we downloaded from Kaggle. Here, we use the Id columns as the dataframe index.

# read csv with a column as index
import pandas as pd
df = pd.read_csv('Iris.csv', index_col='Id')
print(df.head())

Output:

    SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species
Id                                                                       
1             5.1           3.5            1.4           0.2  Iris-setosa
2             4.9           3.0            1.4           0.2  Iris-setosa
3             4.7           3.2            1.3           0.2  Iris-setosa
4             4.6           3.1            1.5           0.2  Iris-setosa
5             5.0           3.6            1.4           0.2  Iris-setosa

In the above example, you can see that the Id column is used as the row index of the dataframe df. You can also pass multiple columns as list to the index_col parameter to be used as row index.

You can also specify the subset of columns to read from the dataset. Pass the subset of columns you want as a list to the usecols parameter. For example, let’s read all the columns from Iris.csv except Id.

# read csv with a column as index
import pandas as pd
df = pd.read_csv('Iris.csv', usecols=['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species'])
print(df.head())

Output:

   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species
0            5.1           3.5            1.4           0.2  Iris-setosa
1            4.9           3.0            1.4           0.2  Iris-setosa
2            4.7           3.2            1.3           0.2  Iris-setosa
3            4.6           3.1            1.5           0.2  Iris-setosa
4            5.0           3.6            1.4           0.2  Iris-setosa

In the above example, the returned dataframe does not have an Id column.

You can also specify the number of rows of a file to read using the nrows parameter to the read_csv() function. Particularly useful when you want to read a small segment of a large file.

# read csv with a column as index
import pandas as pd
df = pd.read_csv('Iris.csv', nrows=3)
print(df.head())

Output:

   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species
0   1            5.1           3.5            1.4           0.2  Iris-setosa
1   2            4.9           3.0            1.4           0.2  Iris-setosa
2   3            4.7           3.2            1.3           0.2  Iris-setosa

In the above example, we read only the first three rows of the file Iris.csv.

These are just some of the things you can do when reading a CSV file to dataframe. Pandas dataframes also provide a number of useful features to manipulate the data once the dataframe has been created.

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having pandas version 1.0.5


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