📚 Free Learning Resources for Data Science 📚

Boost your data science skills with these amazing free courses on Kaggle, taught by industry experts!

1. Python 🐍
- Instructor: Colin Morris
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2. Pandas 🐼
- Instructor: Aleksey Bilogur
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3. Data Visualization 📊
- Instructor: Alexis Cook
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4. Intro to SQL 💾
- Instructor: Alexis Cook
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5. Advanced SQL 🛠️
- Instructor: Alexis Cook
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6. Intro to Machine Learning 🤖
- Instructor: Dan Becker
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7. Intermediate Machine Learning 📈
- Instructor: Alexis Cook
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Take advantage of these resources and start your data science journey today! 🚀

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DAY 4: MEET PANDAS – YOUR DATA MANIPULATION POWERHOUSE 🐼💪

Welcome to Day 4 of our 10-Day Python for Data Analytics Series! 🎉 After exploring NumPy yesterday, today we’ll dive into Pandas, the most popular Python library for working with structured data.

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🛠️ What You’ll Learn Today:
- What Pandas is and why it’s crucial for data analysis
- Creating and exploring DataFrames
- Basic operations for working with data

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1. What is Pandas? 🤔
Pandas is a fast, powerful, and easy-to-use open-source data analysis library in Python. It provides DataFrames, which are like spreadsheets in Python, making it perfect for working with tabular data (rows and columns).

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2. Creating a DataFrame
A DataFrame is a 2D data structure that can store data of different types (like numbers, strings, etc.) in rows and columns.


import pandas as pd

# Creating a simple DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'],
'Age': [25, 30, 35],
'City': ['New York', 'Los Angeles', 'Chicago']}

df = pd.DataFrame(data)
print(df)


🎯 Why It Matters: DataFrames are central to any data analytics workflow, allowing you to easily view, manipulate, and analyze large datasets.

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3. Reading Data from CSV Files
Pandas makes it easy to read data from CSV files, which is one of the most common file formats used in data analytics.


# Reading a CSV file into a DataFrame
df = pd.read_csv('your_file.csv')
print(df.head()) # Displays the first 5 rows


🎯 Why It Matters: CSV files are widely used for storing data, and reading them into Pandas lets you work with large datasets quickly.

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4. Basic DataFrame Operations
Let’s explore some essential functions to help you understand your data.


# Display basic info about the DataFrame
print(df.info())

# View summary statistics for numerical columns
print(df.describe())

# Select specific columns
print(df[['Name', 'Age']])

# Filtering data
print(df[df['Age'] > 30])


🎯 Why It Matters: Being able to quickly summarize, filter, and explore data is crucial for making informed decisions and performing effective data analysis.

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5. Handling Missing Data
Data often comes with missing values, but Pandas makes it easy to handle them.


# Drop rows with missing values
df_clean = df.dropna()

# Fill missing values with a default value
df_filled = df.fillna(0)


🎯 Why It Matters: Handling missing data is a common task in data cleaning, and Pandas provides flexible tools to deal with it efficiently.

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🎯 Why Pandas is a Game-Changer:
Pandas gives you powerful tools to work with large datasets in an intuitive way. Whether it’s loading data, exploring it, or performing complex transformations, Pandas makes data manipulation easy and fast.

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📝 Today’s Challenge:
1. Create a DataFrame with information about five people, including their names, ages, and cities.
2. Filter the DataFrame to show only people older than 25.
3. Load a CSV file into a DataFrame and display the first 5 rows.

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Tomorrow, in Day 5, we’ll explore Data Visualization using Matplotlib and Seaborn to bring your data to life with charts and graphs! 📊

#PythonForDataAnalytics #Pandas #Day4 #LearnPython #DataManipulation #DataFrames #DataAnalysis

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Share your challenges and questions in the comments below! Let’s keep the momentum going! 👇

Welcome to Day 6 of our 10-Day Python for Data Analytics Series! 🎉 Today, we’re diving into one of the most important aspects of data manipulation—Merging and Joining DataFrames. This is essential when you’re working with multiple datasets that need to be combined for analysis.

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🛠️ WHAT YOU’LL LEARN TODAY:
- Merging and joining DataFrames in Pandas
- Different types of joins: inner, outer, left, right
- Real-world examples of merging data

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1. Why Merge Data? 🤔
In data analytics, we often have data spread across multiple tables or files. For example, you might have customer information in one dataset and their purchase history in another. To analyze these together, we need to merge the data into a single DataFrame.

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2. Basic Merge Example
Pandas provides the merge() function to combine DataFrames based on a common column, similar to SQL JOIN operations.


import pandas as pd

# Sample DataFrames
customers = {'CustomerID': [1, 2, 3],
'Name': ['Alice', 'Bob', 'Charlie']}
orders = {'CustomerID': [1, 2, 4],
'OrderAmount': [200, 150, 300]}

df_customers = pd.DataFrame(customers)
df_orders = pd.DataFrame(orders)

# Merging DataFrames on CustomerID
merged_df = pd.merge(df_customers, df_orders, on='CustomerID')
print(merged_df)


🎯 Why It Matters: Merging is crucial when analyzing related data from different sources, making it easy to draw conclusions from multiple datasets.

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3. Types of Joins in Pandas
Pandas allows you to perform different types of joins depending on how you want to combine your data.

# a. Inner Join (default)
Only includes rows with matching keys in both DataFrames.


pd.merge(df_customers, df_orders, on='CustomerID', how='inner')


# b. Left Join
Includes all rows from the left DataFrame and matching rows from the right.


pd.merge(df_customers, df_orders, on='CustomerID', how='left')


# c. Right Join
Includes all rows from the right DataFrame and matching rows from the left.


pd.merge(df_customers, df_orders, on='CustomerID', how='right')


# d. Outer Join
Includes all rows from both DataFrames, filling in missing values with NaN.


pd.merge(df_customers, df_orders, on='CustomerID', how='outer')


🎯 Why It Matters: Choosing the correct type of join is important to ensure you don’t lose valuable data or include unwanted rows in your analysis.

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4. Merging on Multiple Columns
Sometimes, you need to merge DataFrames using more than one key. Pandas allows you to specify multiple columns as the merging key.


# Example with two keys: CustomerID and ProductID
pd.merge(df1, df2, on=['CustomerID', 'ProductID'])


🎯 Why It Matters: Merging on multiple columns provides flexibility, especially when datasets have more complex relationships.

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5. Joining DataFrames
Pandas also has a join() function, which works similarly to merge() but is typically used for joining DataFrames based on their indices.


df1.set_index('CustomerID').join(df2.set_index('CustomerID'))


🎯 Why It Matters: Using join() is efficient when you’re working with indexed data.

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🎯 Why Merging and Joining Are Essential:
Merging and joining DataFrames allows you to unlock hidden insights by combining data from multiple sources. Whether you’re merging sales data with customer info or transactions with product details, mastering this technique is critical for effective data analysis.

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📝 Today’s Challenge:
1. Create two DataFrames: one with customer info and one with their recent purchases. Try using inner, left, and outer joins to see how the data changes.
2. Merge two DataFrames on multiple columns for a more advanced use case.

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Tomorrow, in Day 7, we’ll explore how to clean and preprocess data to prepare it for deeper analysis! 🧹

#PythonForDataAnalytics #Day6 #MergingDataFrames #DataJoin #LearnPython #Pandas #DataAnalysisJourney

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Got questions about merging data? Share them in the comments below! 👇

📊

#PythonForDataAnalytics #Day7 #DataCleaning #Pandas #DataPreprocessing #LearnPython #DataScienceJourney

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Got any questions on cleaning and preprocessing data? Drop them below! 👇

of your choice and observe the summary statistics.
2. Calculate the mean, median, and standard deviation of a column in your dataset.



In Day 9, we’ll dive into Data Visualization using Matplotlib and Seaborn to create visual insights from your data. 🎨📊

#PythonForDataAnalytics #Day8 #DescriptiveStatistics #Pandas #LearnPython #DataScienceJourney

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Got any questions about descriptive statistics? Drop them below! 👇