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Master Pandas with Adaptive AI.

Master Pandas across 24 topics - Foundations, Data Manipulation, and Advanced Pandas - guided by Leyaa's adaptive intelligence.

3Parts
24Topics
200Patterns
50hEst. duration
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Pandas
Pandas Course
3 - part learning journey
Beginner
Foundations
8 topics · 69 patterns
Beginner
Intermediate
Data Manipulation
8 topics · 67 patterns
Intermediate
Advanced
Advanced Pandas
8 topics · 64 patterns
Advanced
Begin Part 1: Foundations
✦ Course Structure

Your Pandas Journey in 3 Parts

Each part builds on the last - a deliberate progression from beginner to advanced.

Beginner
Beginner
Part 1: Foundations
Covers Pandas Fundamentals, Creating DataFrames, Reading and Writing Data and 5 more topics.
8Topics
69Patterns
View topics
Intermediate
Intermediate
Part 2: Data Manipulation
Covers Missing Data, GroupBy Operations, Merging and Joining and 5 more topics.
8Topics
67Patterns
View topics
Advanced
Advanced
Part 3: Advanced Pandas
Covers MultiIndex, Window Functions, Advanced GroupBy and 5 more topics.
8Topics
64Patterns
View topics
Curriculum

Everything You'll Learn

Explore every topic and pattern in the course.

Pandas

3 parts · 24 topics · 200 patterns
Beginner
Part 1: Foundations8 topics · 69 patterns
Beginner
01
Pandas Fundamentals
8 patterns
What is Pandas
Why Pandas for data analysis
Installing Pandas
Importing Pandas conventions
Series as labeled one-dimensional array
DataFrame as labeled two-dimensional table
Series vs DataFrame relationship
Pandas and NumPy connection
02
Creating DataFrames
8 patterns
Why DataFrame creation matters
Creating DataFrame from dictionary
Creating DataFrame from list of lists
Creating DataFrame from list of dictionaries
Creating DataFrame from NumPy array
Specifying column names and index
Creating Series from list and dictionary
dtypes and data type checking
03
Reading and Writing Data
8 patterns
Why data I/O matters
Reading CSV files with read_csv
read_csv parameters (sep, header, index_col)
Writing to CSV with to_csv
Reading Excel files with read_excel
Writing to Excel with to_excel
Reading JSON with read_json
Handling encoding issues
04
Exploring DataFrames
9 patterns
Why data exploration matters
head() and tail() for previewing
shape for dimensions
info() for column types and nulls
describe() for statistical summary
dtypes for column data types
columns and index attributes
value_counts() for frequency
nunique() for unique counts
05
Indexing and Selection
10 patterns
Why indexing matters
Selecting columns by name
Selecting multiple columns
loc for label-based selection
iloc for position-based selection
loc vs iloc mental model
Selecting rows by condition
Boolean indexing
Multiple conditions with & and |
isin() for value matching
06
Data Types and Conversion
9 patterns
Why correct dtypes matter
Numeric types (int64, float64)
String type (object, string)
Datetime type
Category type for efficiency
astype() for type conversion
to_numeric() for safe conversion
to_datetime() for date parsing
Common dtype errors and fixes
07
Basic Operations
9 patterns
Why vectorized operations matter
Arithmetic operations on columns
Creating new columns
Renaming columns
Dropping columns and rows
Sorting by values
Sorting by index
Resetting index
Setting a column as index
08
String Operations
8 patterns
Why string operations matter
str accessor for string methods
str.lower() and str.upper()
str.contains() for pattern matching
str.replace() for substitution
str.split() for splitting
str.strip() for whitespace
str.len() for string length
Intermediate
Part 2: Data Manipulation8 topics · 67 patterns
Intermediate
01
Missing Data
9 patterns
Why handling missing data matters
NaN and None in Pandas
Detecting missing values with isna()
Counting missing values
Dropping missing values with dropna()
Filling missing values with fillna()
Forward fill and backward fill
Interpolation for missing values
Missing data strategies decision
02
GroupBy Operations
10 patterns
Why grouping data matters
Split-apply-combine mental model
groupby() basics
Aggregation with agg()
Multiple aggregation functions
Named aggregation
transform() for group-level operations
filter() for group-level filtering
Grouping by multiple columns
Iterating over groups
03
Merging and Joining
10 patterns
Why combining DataFrames matters
merge() for SQL-like joins
Inner join behavior
Left join behavior
Right join behavior
Outer join behavior
Merging on different column names
Merging on index
concat() for stacking DataFrames
append equivalent with concat
04
Pivot Tables and Reshaping
8 patterns
Why reshaping data matters
pivot_table() for summarization
Pivot with aggregation functions
melt() for unpivoting
stack() and unstack()
Wide to long format conversion
Long to wide format conversion
crosstab() for cross-tabulation
05
Apply and Map
8 patterns
Why custom functions matter
apply() on columns
apply() on rows (axis=1)
apply() with lambda functions
map() for element-wise transformation
applymap() for DataFrame-wide operations
replace() for value substitution
When to use apply vs vectorized operations
06
Datetime Operations
9 patterns
Why datetime handling matters
to_datetime() for parsing dates
dt accessor for datetime properties
Extracting year, month, day
Extracting day of week and hour
Date arithmetic with timedelta
Resampling time series data
Date range creation with date_range
Timezone handling basics
07
Sorting and Ranking
7 patterns
Why sorting and ranking matter
sort_values() by single column
sort_values() by multiple columns
Ascending and descending order
sort_index() for index sorting
rank() method and ranking methods
nlargest() and nsmallest()
08
Duplicate Handling
6 patterns
Why duplicate detection matters
duplicated() for finding duplicates
drop_duplicates() for removal
Keeping first vs last vs none
Duplicates on specific columns
Counting duplicates
Advanced
Part 3: Advanced Pandas8 topics · 64 patterns
Advanced
01
MultiIndex
8 patterns
Why MultiIndex enables hierarchical data
Creating MultiIndex DataFrames
Selecting data with MultiIndex
xs() for cross-section selection
Swapping index levels
Resetting MultiIndex to columns
Setting columns as MultiIndex
Sorting MultiIndex
02
Window Functions
9 patterns
Why window functions matter
rolling() for moving windows
Rolling mean and sum
Rolling standard deviation
Expanding window operations
ewm() for exponential moving average
shift() for lagging data
diff() for differences
pct_change() for percentage change
03
Advanced GroupBy
7 patterns
Why advanced grouping matters
GroupBy with custom functions
GroupBy with transform for normalization
GroupBy with pipe for chaining
Resampling with groupby for time data
Cross-tabulation advanced usage
GroupBy performance considerations
04
Categorical Data
7 patterns
Why categorical type matters
Converting to categorical
Ordered categories
Category codes and labels
Adding and removing categories
Binning with cut() and qcut()
Memory savings with categoricals
05
Performance Optimization
9 patterns
Why Pandas performance matters
Vectorized operations vs loops
eval() for expression evaluation
query() for fast filtering
Using appropriate dtypes
Chunked reading for large files
Memory usage analysis
Inplace operations consideration
When to use NumPy over Pandas
06
Data Cleaning Patterns
8 patterns
Why systematic cleaning matters
Standardizing column names
Handling inconsistent values
Outlier detection with IQR
Data validation checks
Regex operations in Pandas
Combining multiple cleaning steps
Building cleaning pipelines with pipe()
07
Visualization with Pandas
8 patterns
Why built-in plotting matters
Line plots with plot()
Bar plots
Histogram plots
Scatter plots
Box plots
Plot customization (title, labels, figsize)
Subplots for multiple charts
08
Real-World Analysis Patterns
8 patterns
Why end-to-end analysis matters
Exploratory data analysis workflow
Feature engineering basics
Data aggregation reporting
Time series analysis patterns
Exporting results to multiple formats
Pandas with SQL databases
Working with large datasets strategies
Why Leyaa

Not just a course. A learning intelligence.

Leyaa doesn't just deliver content - it understands where you are, what you need, and how to get you there fastest.

Adaptive Diagnostics
Adaptive Diagnostics
Leyaa identifies which of the 200 patterns you've mastered and which need work - before you waste time on what you already know.
Pattern-Level Precision
Pattern-Level Precision
Every pattern gets a verdict - Learn, Practice, or Skip - so your study time goes exactly where it counts.
Intelligent Pacing
Intelligent Pacing
Leyaa reads your velocity across the whole course and adapts your focus list dynamically, keeping you in the optimal learning zone.
Before & After Visibility
Before & After Visibility
Every assessment creates a snapshot. Retake anytime to see exactly how much you've grown - topic by topic, pattern by pattern.
Learner Autonomy
Learner Autonomy
Leyaa recommends, never restricts. Override any suggestion and explore at your own pace - the system tracks everything regardless.
Clear Learning Path
Clear Learning Path
Foundations → Data Manipulation → Advanced Pandas - a structured journey where each part sets you up perfectly for the next.
Pandas

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24 Topics
200 Patterns
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