Exploring Data [2/3]
DataFrame size
To get the dimensionality of DataFrame (i.e., number of rows and columns), use the .shape attribute. It will return a tuple (immutable list-like structure) with 2 values: the first one is the number of rows, the second one is the number of columns.
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' dimensionality print(df.shape)
Values' types
Before aggregating and visualizing data, you need to understand are these data have appropriate formats. For example, you may face the situation when prices will be represented in text form - this will make impossible to aggregate it. To get the columns values' types, use the .dtypes attribute.
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') # Columns values' types print(df.dtypes)
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DataFrame size
To get the dimensionality of DataFrame (i.e., number of rows and columns), use the .shape attribute. It will return a tuple (immutable list-like structure) with 2 values: the first one is the number of rows, the second one is the number of columns.
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' dimensionality print(df.shape)
Values' types
Before aggregating and visualizing data, you need to understand are these data have appropriate formats. For example, you may face the situation when prices will be represented in text form - this will make impossible to aggregate it. To get the columns values' types, use the .dtypes attribute.
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') # Columns values' types print(df.dtypes)
Merci pour vos commentaires !
