Challenge 1: DataFrame Creation

Pandas, a powerful data manipulation library in Python, provides multiple efficient and intuitive methods to create DataFrames. The advantages of using these methods include:

Versatility: Pandas offers a variety of ways to create DataFrames from different types of data sources. This ensures flexibility based on data availability and format.
Ease of use: The syntax for creating DataFrames is clear and consistent, simplifying data wrangling tasks.
Integration: DataFrames can easily be converted to and from other data structures, promoting interoperability with different libraries.

In the realm of data science and analytics, Pandas' DataFrame creation tools guarantee both convenience and consistency in your data processing workflow.

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Create a Pandas DataFrame using three different methods:

Read data from a CSV file.
Create a DataFrame from a NumPy array. Column names must be A, B and C.
Construct a DataFrame from a Python dictionary.

Løsning

import pandas as pd

import numpy as np

# 1. Read data from a CSV file.

file_path = 'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/8f74b411-aed3-4916-8a1a-b25629653d8e/4/dataframe.csv'

csv_df = pd.read_csv(file_path)

display(csv_df)

# 2. Create a DataFrame from a NumPy array.

array_data = np.array([[1, 4, 7], [2, 5, 8], [3, 6, 9]])

array_df = pd.DataFrame(array_data, columns=['A', 'B', 'C'])

display(array_df)

# 3. Construct a DataFrame from a Python dictionary.

dict_data = {'A_': [1, 2, 3], 'B_': [4, 5, 6], 'C_': [7, 8, 9]}

dict_df = pd.DataFrame(dict_data)

display(dict_df)

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Seksjon 3. Kapittel 1

import pandas as pd

import numpy as np

# 1. Read data from a CSV file.

file_path = 'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/8f74b411-aed3-4916-8a1a-b25629653d8e/4/dataframe.csv'

csv_df = ___

display(csv_df)

# 2. Create a DataFrame from a NumPy array.

array_data = np.array([[1, 4, 7], [2, 5, 8], [3, 6, 9]])

array_df = ___

display(array_df)

# 3. Construct a DataFrame from a Python dictionary.

dict_data = {'A_': [1, 2, 3], 'B_': [4, 5, 6], 'C_': [7, 8, 9]}

dict_df = ___

display(dict_df)

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