The next step we need to do is to replace dots. This task is a bit harder than the previous one, since you will replace only specific elements.

First, let's remind how to select specific rows and columns based on some condition. It can be done by applying the `.loc[]` property. The first parameter is either row numbersm, or condition; the second one is column names. For instance, let's get the rows containing only dot characters `.` within the `'morgh'` column.

# 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')
# Output only dot values within the 'morgh' column
print(df.loc[df.morgh == '.', 'morgh'])

Since we accessed the necessary rows, we can easily replace them by reassigning. We are going to repalce all the dots by `NA` values (`nan` from `NumPy`) and then convert the resulting column to `float` type (`NA` doesn't support `int` type, `float` only).

# Importing libraries
import pandas as pd
import numpy as np

# Reading the file
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/f2947b09-5f0d-4ad9-992f-ec0b87cd4b3f/data1.csv')
# Perform a replacement
df.loc[df.morgh == '.', 'morgh'] = np.nan
# Converting
df.morgh = df.morgh.astype(float)
print(df.morgh)

As you can see, the column is now considered to have `float` type, which means you can apply numerical methods to it (i.e., you can calculate mean, min, max, etc.).

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.

Replacing Specific Elements

Awesome!

Replacing Specific Elements