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Leer Exploring Data [2/3] | Reading and Exploring Data
Introduction to pandas [track]
course content

Cursusinhoud

Introduction to pandas [track]

Introduction to pandas [track]

1. Basics
2. Reading and Exploring Data
3. Accessing DataFrame Values
4. Aggregate Functions

book
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.

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# 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)
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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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Sectie 2. Hoofdstuk 5

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course content

Cursusinhoud

Introduction to pandas [track]

Introduction to pandas [track]

1. Basics
2. Reading and Exploring Data
3. Accessing DataFrame Values
4. Aggregate Functions

book
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)
copy

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)
copy

Was alles duidelijk?

Hoe kunnen we het verbeteren?

Bedankt voor je feedback!

Sectie 2. Hoofdstuk 5
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