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Combining multiple Series into a DataFrame Combining multiple Series to form a DataFrame Converting a Series to a DataFrame Converting list of lists into DataFrame Converting list to DataFrame Converting percent string into a numeric for read_csv Converting scikit-learn dataset to Pandas DataFrame Converting string data into a DataFrame Creating a DataFrame from a string Creating a DataFrame using lists Creating a DataFrame with different type for each column Creating a DataFrame with empty values Creating a DataFrame with missing values Creating a DataFrame with random numbers Creating a DataFrame with zeros Creating a MultiIndex DataFrame Creating a Pandas DataFrame Creating a single DataFrame from multiple files Creating empty DataFrame with only column labels Filling missing values when using read_csv Importing Dataset Importing tables from PostgreSQL as Pandas DataFrames Initialising a DataFrame using a constant Initialising a DataFrame using a dictionary Initialising a DataFrame using a list of dictionaries Inserting lists into a DataFrame cell Keeping leading zeroes when using read_csv Parsing dates when using read_csv Preventing strings from getting parsed as NaN for read_csv Reading data from GitHub Reading file without header Reading large CSV files in chunks Reading n random lines using read_csv Reading space-delimited files Reading specific columns from file Reading tab-delimited files Reading the first few lines of a file to create DataFrame Reading the last n lines of a file Reading URL using read_csv Reading zipped csv file as a DataFrame Removing Unnamed:0 column Resolving ParserError: Error tokenizing data Saving DataFrame as zipped csv Skipping rows without skipping header for read_csv Specifying data type for read_csv Treating missing values as empty strings rather than NaN for read_csv

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Importing Dataset in Pandas

schedule Aug 10, 2023

Last updated

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Pandas●Python

Datasets with Column Labels and Indices

Suppose we wanted to import the following file called my_file.csv:

Note the following:

the column labels are a, b and c.
the row labels are A and B.

To read this file as a DataFrame:


        
        
            
                
                
                    import pandas as pd

df = pd.read_csv("my_file.csv")
print(df)
                
            
               a  b  c
A  1  2  3
B  4  5  6

Note that this code assumes the file to be located in the same directory.

Datasets with Only Values

Suppose our CSV dataset only consisted of values:

To import this file, we need to add the header=None option:


        
        
            
                
                
                    df = pd.read_csv("my_file.csv", header=None)
print(df)
                
            
               0  1  2
0  1  2  3
1  4  5  6

As we can see, Pandas uses the default integer indices for its column and row labels.

Datasets with Custom Delimiters

The read_csv() method can also be used to import datasets that use delimiters other than comma. We can specify the delimiter by supplying the delimiters parameter.

As an example, suppose our dataset uses a single blank space as the delimiter:

To read this file:


        
        
            
                
                
                    df = pd.read_csv("my_file.csv", delimiter=" ")
print(df)
                
            
               a  b  c
A  1  2  3
B  4  5  6