Pandas Read_Table()

Pandas come with various tools to read the data from links and files, which we can use in our development or research process. We will go through one method: the read_table() and its few examples.

What Is the Read_Table() Method

Pandas is a popular data analysis, exploration, and manipulation tool. We regularly use the URLs and files to do the different activities while researching in real-world data. Pandas provide multiple tools. One of its approaches is the read_table(). It is quite similar to the Pandas read_csv() method. Like the read_csv() method, this also reads the given table, including the DataFrames. In addition, we can also specify various options to get customized rows from the given table.

Examples of Read_Table() Method

Example #1

With columns separated by ‘,’ display the whole file contents.

# importing pandas

import pandas as pd

pd.read_table('file.csv',delimiter=',')

Example #2

Skipping rows without updating the indexes of the rows

# importing pandas

import pandas as pd

pd.read_table('file.csv',delimiter=',',skiprows=5,index_col=0)

Example #3

The following code allows you to skip rows with updated indexes:

# importing pandas

import pandas as pd

pd.read_table('file.csv',delimiter=',',skiprows=5)

Example #4

If you simply wish to read the top few lines, set the nrows option to the appropriate number of lines.

# importing pandas

import pandas as pd

pd.read_table('file.csv',delimiter=',', index_col=0, nrows=5)

Example #5

Set the skipfooter option to the required number from the bottom to skip the lines as shown in the following command:

# importing pandas

import pandas as pd

pd.read_table('file.csv',delimiter=',',index_col=0, engine='python', skipfooter=5)

Conclusion

We discussed the description and examples of the Pandas read_table() method, which reads the tables from files and links. We also learned how we could skip and get customized rows from the given input according to our needs.