Data Engineering · Data Management · Pandas · Python

Pandas Basic Commands


In this blogpost, we will see some of the Python’s pandas library basic commands and its operations. For running below commands, here, I have used Azure Databricks Notebook with python language. Via magic commands(%python), we can use the same below commands under other language connected notebooks as well.

Basic pandas commands

1. Importing pandas library

we can also import numpy with pandas to utilize much benefits. In below commands, we will see some.

2. To read file and creating a DataFrame

we will use above sample file data for our understandings.

3. To display first 1000 rows

4. To check the type of the DataFrame + type of the column in the DataFrame

5. To check the first/last n entries + default + To check the first/last n entries on the DataFrame Column

6. To check the dimensions of our data

where 1460 is number of rows and 16 is number of columns.

7. To view a summary of the data set/DataFrame

8. To view descriptive statistics about the dataset

describe function works only on numeric kind of data types.

9. To return a Series containing the number of unique values

10. DataFrame Index

11. To check the column names of the dataset/DataFrame + to know row/column count of the dataset/DataFrame

12. To rename multiple columns

13. To create a copy of our DataFrame

14. To add a column with default value

15. To add multiple columns with default values

Here we have used np.nan which means numpy library’s null equivalent.

16. To add n rows in DataFrame

17. To remove the rows + columns

18. To Select data to bring all columns + selected columns + to apply filter condition + bring selected columns

19. To select the data contained in the first row and the first column + entire row + last column + multiple rows and columns combo

20. To Detect missing values as data + count + proportions

21. To Remove missing values

22. To Fill the missing values with default value + median value

Here, instead of median function, we can use other aggregate functions as well. examples are count, min, max, sum etc.,

23. Histograms – to display the distribution of data

24. Scatter plots – to visualize the relationship between two variables

25. To save our DataFrame as a file

We can able to provide our path where we need to save the result set DataFrame.


Thus in this blogpost, we saw above 25 basic pandas library operations and commands. If you thought the above code that may be helpful for you, please take it here(github) and enjoy.

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