Summary of "Learn Python for Data Science – Full Course for Beginners"
Summary of Main Ideas and Concepts
1. Introduction to Python for Data Science
- Course covers Python libraries essential for data science: Pandas, NumPy, data visualization, data cleaning, and machine learning.
- Includes hands-on exercises and projects.
- Setup instructions for Anaconda (includes Python, Jupyter Notebook, Pandas, etc.).
- Introduction to Jupyter Notebook interface and usage:
- Creating notebooks, cells, cell types (code, markdown, raw).
- Command mode vs edit mode.
- Keyboard shortcuts for efficient workflow.
- Managing running notebooks and extensions.
2. Python Basics
- Printing messages, strings, and string methods (upper, lower, title, count, replace).
- Variables and string concatenation (including f-strings).
- Lists:
- Creation, indexing (positive and negative), slicing.
- Adding (append, insert), removing (remove, pop, del), sorting, updating, copying.
- Nested lists.
- Dictionaries:
- Creation with key-value pairs.
- Accessing keys, values, items.
- Adding, updating, copying, removing elements.
- Conditional statements (
if,elif,else) and loops (for). - Functions:
- Defining and calling functions.
- Built-in Python functions (
len,max,min,type,range).
- Modules:
- Importing and using built-in modules like
os.
- Importing and using built-in modules like
3. Pandas for Data Science
- Why Pandas over Excel:
- Handles larger datasets.
- Better for complex transformations and automation.
- Cross-platform compatibility.
- Core Pandas concepts:
- Series (1D array), DataFrame (2D array).
- Rows = observations, columns = features/series.
- Indexing and NaN (missing data).
- Creating DataFrames:
- From NumPy arrays, lists, dictionaries.
- Reading CSV files.
- Displaying DataFrames:
.head(),.tail(),.shape,.set_option()to view all rows.
- DataFrame attributes, methods, and functions:
.columns,.dtypes,.info(),.describe(),.value_counts().
- Selecting columns:
- Single column (square brackets or dot notation with caveats).
- Multiple columns (double square brackets).
- Adding new columns:
- Assigning scalar values, arrays (NumPy), random numbers.
.assign()and.insert()methods.
- DataFrame operations:
- Column-wise:
.sum(),.count(),.mean(),.std(),.max(),.min(). - Row-wise: sum and average across multiple columns.
- Column-wise:
- Sorting DataFrames:
.sort_values()with multiple columns, ascending/descending,inplaceoption.
- Index manipulation:
.set_index(),.sort_index().
- Renaming columns and indexes with
.rename(). - Filtering DataFrames based on conditions:
- Single and multiple conditions using boolean indexing.
.where()method for conditional assignment..select()method for multiple conditions..isin()method for filtering by list of values.
- Handling duplicates:
.duplicated(),.drop_duplicates(),keepparameter.
- Getting unique values:
.unique(),.nunique().
- Selecting data by label and position:
.loc[](label-based) and.iloc[](position-based).- Slicing and conditional selection with
.locand.iloc.
- Copying DataFrames:
.copy()method withdeepparameter.
- Pivot tables:
.pivot()(reshaping without aggregation)..pivot_table()(with aggregation, similar to Excel pivot tables).
- Data visualization with Pandas:
- Line plot, bar plot, pie chart, box plot, histogram, scatter plot.
- Customizing plots (labels, title, color, size).
- Exporting plots and DataFrames.
- Interactive visualizations with Plotly and Cufflinks:
- Installation and setup.
- Using
.iplot()method instead of.plot(). - Interactive line plots, bar plots, pie charts, box plots, histograms, scatter plots.
- Advantages of interactive plots (zoom, hover data, toggle visibility).
4. Grouping and Aggregation
- Split-Apply-Combine strategy explained.
- Using
.groupby()for grouping data and applying aggregate functions. - Aggregation functions:
sum(),mean(),count(),min(),max(). - Applying
Category
Educational
