Superficial Analysis
Welcome to the third section of the course! Here, you will use the pandas library to analyze data by answering specific questions.
Let's start with some basic information. We are going to analyze and visualize data on store sales. First, we need to determine how many stores we are working with. We can use the .describe() method of pandas to show the main descriptive statistics on numerical columns.
Also, we can use the .unique() method of pandas to get only unique values of a certain column.
12345678910# 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_data3.csv') df['Date'] = pd.to_datetime(df['Date'], dayfirst = True) # Inspecting the data print(df.describe()) print(df.Store.unique())
For the following question, feel free to edit the code above if necessary.
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Welcome to the third section of the course! Here, you will use the pandas library to analyze data by answering specific questions.
Let's start with some basic information. We are going to analyze and visualize data on store sales. First, we need to determine how many stores we are working with. We can use the .describe() method of pandas to show the main descriptive statistics on numerical columns.
Also, we can use the .unique() method of pandas to get only unique values of a certain column.
12345678910# 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_data3.csv') df['Date'] = pd.to_datetime(df['Date'], dayfirst = True) # Inspecting the data print(df.describe()) print(df.Store.unique())
For the following question, feel free to edit the code above if necessary.
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