Python Frameworks for Data Science

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Well, imagine it like this: you have a big pile of Legos and you want to build something cool. You could spend hours figuring out how all the pieces fit together or you could use a pre-made set that already has instructions on how to put everything together. That’s what a framework is it gives you a starting point for building your own custom data analysis tool using Python.
One popular framework for data science in Python is called Pandas. It’s like the Swiss Army knife of data manipulation because it can do pretty much anything you need when working with large datasets. For example, let’s say you have a dataset that looks something like this:

# Import the pandas library and alias it as "pd"
import pandas as pd

# Create a dictionary called "data" with three key-value pairs
data = { 'Name': ['Alice', 'Bob', 'Charlie'], # Key: "Name", Value: List of names
         'Age': [25, 30, 40], # Key: "Age", Value: List of ages
         'Gender': ['Female', 'Male', 'Male'] } # Key: "Gender", Value: List of genders

# Use the pandas DataFrame function to create a new dataframe called "df" using the data dictionary
df = pd.DataFrame(data)

# Print the dataframe "df" to the console
print(df)

This code creates a dictionary called `data`, which contains the names of our columns and their corresponding values for each row in our dataset. We then use Pandas to create a DataFrame object (which is like a spreadsheet) from this data using the `pd.DataFrame()` function. Finally, we print out the resulting DataFrame so that we can see what it looks like:

# Create a dictionary with column names and their corresponding values
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 40], 'Gender': ['Female', 'Male', 'Male']}

# Use Pandas to create a DataFrame object from the dictionary
df = pd.DataFrame(data)

# Print out the resulting DataFrame
print(df)

# The code above creates a dictionary with the names of our columns and their corresponding values for each row in our dataset. 
# Then, it uses the `pd.DataFrame()` function to create a DataFrame object (which is like a spreadsheet) from this data. 
# Finally, it prints out the resulting DataFrame so that we can see what it looks like.

Pretty cool right? Pandas also has a ton of built-in functions for data manipulation and analysis. For example:

# Import the pandas library and assign it to the alias "pd"
import pandas as pd

# Create a dictionary with data for three individuals
data = { 'Name': ['Alice', 'Bob', 'Charlie'],
         'Age': [25, 30, 40],
         'Gender': ['Female', 'Male', 'Male'] }

# Create a DataFrame using the data dictionary
df = pd.DataFrame(data)

# Print the "Name" column of the DataFrame as a Series object (like a list)
print(df['Name'])

This code uses Pandas to select only the `’Name’` column from our DataFrame and print it out using the `Series()` function. The resulting output looks like this:

# Import the Pandas library
import pandas as pd

# Create a DataFrame with three columns: 'Name', 'Age', and 'Gender'
df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35], 'Gender': ['Female', 'Male', 'Male']})

# Select only the 'Name' column from the DataFrame and store it in a variable
names = df['Name']

# Use the Series() function to print out the 'Name' column as a Series object
print(names)

# Output:
# 0    Alice
# 1     Bob
# 2  Charlie
# Name: Name, dtype: object

If you want to learn more about Python frameworks for data science and how they can help you with your own projects, be sure to check out some of the resources I mentioned earlier. And if you have any questions or comments, feel free to let me know in the comments section below!

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