All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.
All our activities include solutions with explanations on how they work and why we chose them.
Perform the calculation and store the results in the variable col_missing_values.
You have to drop this column permanently as we can not use it for any purpose.
Store the resulting DataFrame in the variable df_rows_dropped.
Store your result in the variable host_total.
Store your result in the variable mean_df_price.
Store your result in the variable ffill_review.
Store your result in the variable duplicate_hosts.
Perform the dropping and store the results in the variable df_unique_hosts.
Let's count all the Private rooms in the column room_type and sum them up.
Store your sum in the private_rooms_counts variable.
Store your selection in the variable names_having_park.
Store the output in the variable kitchen_to_restaurant.
Store them in the variable roomOrhome
Note: We are interested in the value at second index once you split all the strings in the Column room_type on space.
Invalid values are defined as any host that offers a value of 0 in availability_365.
Perform the selection of invalid values and store the results in the variable df_invalid_availability.
Outliers are defined as any values 4 or more std to the left or right of the mean.
Perform the outlier identification and store the results in a new column df_nights['Min_Nights_cleaned'].
Outliers are defined as any values that are 1.5 IQR to the left or right.
Perform the outlier identification and store the results in a new column df_Price['Price_cleaned'].
