The method described in the previous chapter allows you to replace specific values in one column 'manually'. But we need to perform replacements in 4 columns, which means we need to repeat the actions at least 3 more times.

However, `pandas` predicted that task, too. Let's consider the method that allows to perform replacement for all dataframe columns.

df.where(condition, other = values_to_replace, inplace = False)

Explanation: `condition` is the first parameter, if `True`, then keeps original values, if `False`, then replaces them by values specified in the `other` parameter. `inplace` - if `True`, then rewrites the data. If you want to 'revert' the condition to opposite, place the `~` symbol in front of it. For instance, let's replace all the zeros with the word `null`.

# Importing the library
import pandas as pd

# Reading the file
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/f2947b09-5f0d-4ad9-992f-ec0b87cd4b3f/data1.csv')
# Replace 0s by words 'null'
df = df.where(~(df == 0), other = 'null')
print(df)

As you can see, there are many `'null'`s appeared in the dataframe. If you remove the `~` symbol within the `.where()` method, then all values but `0` will be replaced to `'null'`.

This course covers intermediate topics on pandas, a must-have tool for each data analyst. During the course, you will learn how to prepare data for further interactions and how to group it using different techniques. You will learn the easiest data visualization and be acquainted with data joining.

The data received from different sources can be messy, and to use it in the future, you must ensure it is convenient. In the first section, you will learn what data preprocessing is and will deal with some logical inconsistencies.

Missing or NA values, outliers, and inconsistencies are other types of problematic data. Throughout the second section of the course, you will learn how to deal with such issues.

As a data analyst, you will need to draw compact conclusions based on large amounts of data. In order to achieve that, you need to understand the data grouping idea and how to apply it to examples.

Sometimes one built-in function is not enough to draw a complete conclusion, so you need to use something more complex. This section will teach you how to apply multiple functions while grouping. Also, you will learn to visualize data using the pandas library only and will be acquainted with the main plot types. As a data analyst, you will need to draw compact conclusions based on large amounts of data. In order to achieve that, you need to understand the data grouping idea and how to apply it to examples.

As mentioned before, sometimes you may need to work with data received from multiple sources. This section will teach you how to join two dataframes using different techniques.

Simultaneous Replacement

Awesome!

Simultaneous Replacement