Exploring Data [3/3]
Summary of DataFrame' columns
If you need additional information about DataFrame, i.e., memory usage, number of non-null values in addition to the considered in the previous chapter, use the .info()
method.
1234567# Importing library import pandas as pd # Reading csv file df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/67798cef-5e7c-4fbc-af7d-ae96b4443c0a/audi.csv') # DataFrame' columns information print(df.info())
Numerical columns' summary
For numerical columns you can get the mean, minimal, maximal values, 25%, 50%, 75% quantiles, standart deviation using the .describe()
method.
1234567# Importing library import pandas as pd # Reading csv file df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/67798cef-5e7c-4fbc-af7d-ae96b4443c0a/audi.csv') # Numerical columns' summary print(df.describe())
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Exploring Data [3/3]
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Summary of DataFrame' columns
If you need additional information about DataFrame, i.e., memory usage, number of non-null values in addition to the considered in the previous chapter, use the .info()
method.
1234567# Importing library import pandas as pd # Reading csv file df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/67798cef-5e7c-4fbc-af7d-ae96b4443c0a/audi.csv') # DataFrame' columns information print(df.info())
Numerical columns' summary
For numerical columns you can get the mean, minimal, maximal values, 25%, 50%, 75% quantiles, standart deviation using the .describe()
method.
1234567# Importing library import pandas as pd # Reading csv file df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/67798cef-5e7c-4fbc-af7d-ae96b4443c0a/audi.csv') # Numerical columns' summary print(df.describe())
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