Columns Overview
We can see that only two types of data are presented in the dataframe: object and int64. Columns that have an object data type contain string objects, which makes aggregation impossible for them. Among these columns are Weekly_Sales, Temperature, Fuel_Price, and others. It is obvious that all the mentioned columns must be numerical. We might be interested in comparing the revenue for different dates, but with object data, it's impossible.
Let's solve problems step by step. First, let's remind ourselves what our data looks like by outputting a single row.
12345678# Loading the library import pandas as pd # Reading the data df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/72be5dde-f3e6-4c40-8881-e1d97ae31287/shops_data_init.csv') # Displaying a single dataframe row print(df.sample())
Pay close attention to the values and try to find out why most of the columns are considered object columns.
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We can see that only two types of data are presented in the dataframe: object and int64. Columns that have an object data type contain string objects, which makes aggregation impossible for them. Among these columns are Weekly_Sales, Temperature, Fuel_Price, and others. It is obvious that all the mentioned columns must be numerical. We might be interested in comparing the revenue for different dates, but with object data, it's impossible.
Let's solve problems step by step. First, let's remind ourselves what our data looks like by outputting a single row.
12345678# Loading the library import pandas as pd # Reading the data df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/72be5dde-f3e6-4c40-8881-e1d97ae31287/shops_data_init.csv') # Displaying a single dataframe row print(df.sample())
Pay close attention to the values and try to find out why most of the columns are considered object columns.
Дякуємо за ваш відгук!
