Let's see what are the differences that caused these issues by displaying these rows.

# 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/data2.csv')
# Select rows with discrepancies
ind = df.iloc[:,2:15].sum(axis = 1) != df.hhsize
print(df.loc[ind, df.columns[1:15]])

These are not consequent observations, and it's only half a percent of all observations, so we can easily delete them. If you want to delete rows or columns, apply the `.drop()` method to dataframe. If you want to remain changes saved, either reassign to dataframe result of applying method, or set the `inplace = True` parameter. If you want to drop rows, set the `index` parameter to indexes of rows you want to remove, if you want to delete columns - set the `columns` parameter to list of columns you want to delete. For instance, if you want to delete the first, and the third rows, you should apply the `.drop(index = [0, 2])` method. If you want to delete 3-5 columns, then you can get their names from the `.columns` attribute, and apply the `.drop(columns = df.columns[2:5])` method. Feel free to experiment!

# 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/data2.csv')
# Dropping rows

print(dr.drop(index = [0, 2]))
# Dropping columns
print(df.drop(columns = df.columns[2:5]))

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.