๐บ๏ธ Pandas Learning Roadmap for Aspiring Data Scientists
๐งฑ 1. Basics of Pandas
Get comfortable with how pandas works and its core structures.
Topics to Cover:
- Installing and importing pandas
-
Data structures:
-
Creating DataFrames:
- From dictionaries, lists, NumPy arrays
- Reading from CSV, Excel, JSON, SQL
๐๏ธ 2. Data Exploration & Summary
Learn how to inspect and understand the dataset.
Topics to Cover:
.head()
, .tail()
, .info()
, .describe()
-
Checking:
- Data types:
.dtypes
- Null values:
.isnull().sum()
- Unique values and value counts:
.unique()
, .nunique()
, .value_counts()
- Shape and column names
๐ 3. Data Selection & Indexing
Learn how to access and manipulate rows and columns.
Topics to Cover:
- Selecting columns:
df['col']
, df.col
- Selecting rows:
.loc[]
, .iloc[]
- Conditional selection:
df[df['col'] > x]
- Setting and resetting index:
.set_index()
, .reset_index()
- Filtering with multiple conditions
โ๏ธ 4. Data Cleaning
Clean and prepare data for analysis or modeling.
Topics to Cover:
-
Handling missing values:
.isnull()
, .fillna()
, .dropna()
- Renaming columns:
.rename()
- Changing data types:
.astype()
- String operations:
.str.lower()
, .str.contains()
, .str.strip()
- Replacing values:
.replace()
- Removing duplicates:
.drop_duplicates()
Modify data for analysis or modeling.
Topics to Cover:
-
Applying functions:
.apply()
, .map()
, .applymap()
- Lambda functions
- Discretization & binning
- Sorting:
.sort_values()
, .sort_index()
- Combining columns or splitting strings
๐งฎ 6. Aggregation & Grouping
Group and summarize data for insights.
Topics to Cover:
.groupby()
operations
- Aggregations:
.sum()
, .mean()
, .count()
, .agg()
- Multi-level grouping
- Pivot tables:
.pivot_table()
, .pivot()
- Crosstab:
pd.crosstab()
๐ 7. Merging, Joining & Concatenation
Work with multiple datasets.
Topics to Cover:
pd.concat()
โ stacking vertically/horizontally
pd.merge()
โ SQL-like joins (inner, outer, left, right)
df.join()
โ joining on index
- Aligning data with different indexes
๐ฐ๏ธ 8. Working with Date and Time
Handle time series data.
Topics to Cover:
pd.to_datetime()
- Extracting date/time components:
.dt.year
, .dt.month
, etc.
- Filtering by date range
- Resampling time series:
.resample()
- Shifting/rolling:
.shift()
, .rolling()
๐ 9. Data Visualization (with Pandas)
Basic visualizations to explore data.
Topics to Cover:
- Line plots:
.plot()
- Histograms:
.hist()
- Bar plots:
.plot(kind='bar')
- Box plots
- Area and pie charts
๐น Later, transition to using Seaborn or Matplotlib for more advanced plots.
Read from or write to different file types.
Topics to Cover:
- CSV:
pd.read_csv()
, .to_csv()
- Excel:
pd.read_excel()
, .to_excel()
- JSON:
pd.read_json()
- SQL databases:
pd.read_sql()
- Clipboard, HTML, Pickle
โ๏ธ 11. Advanced Features & Optimization
Improve performance and write cleaner code.
Topics to Cover:
- Chaining methods
- Method chaining vs. assigning intermediate steps
- Vectorization vs. loops
- Memory optimization:
df.memory_usage()
- Categorical data type
- Using
.query()
for readable filtering
๐ฏ 12. Practical Use Cases & Projects
Apply what you learned in real datasets.
Suggested Projects:
- Sales/retail dataset analysis
- COVID-19 or weather data analysis
- Movie ratings (IMDb/TMDB)
- Stock market analysis (time series)
- EDA (exploratory data analysis) on Kaggle datasets
โ
Tips for Learning Pandas:
- Practice each concept on small datasets.
- Regularly use real-world datasets from Kaggle, UCI, or public APIs.
- Focus on writing clean, readable code.
Thanks