Python for statistical analysis
- 1. Python for Statistical Analysis AND ITS DIFFERENT PACKAGES 18SE02CE011 : URJA DIYORA SUBMIT TO : DR.JASLEEN KAUR
- 2. OUTLINE • Introduction to Pandas • Data Wrangling with Pandas • Plotting and Visualization • NumPy Basics: Arrays and Vectorized Computation • Statistical Data Modeling • Data Loading, Storage, and File Formats • Packages For Statistical Analysis
- 3. Introduction to Pandas • Importing data • Series and DataFrame objects • Indexing, data selection and subsetting • Hierarchical indexing • Reading and writing files • Sorting and ranking • Missing data • Data summarization
- 4. Data Wrangling with Pandas • Date/time types • Merging and joining DataFrame objects • Concatenation • Reshaping DataFrame objects • Pivoting • Data transformation • Permutation and sampling • Data aggregation and GroupBy operation
- 5. Plotting and Visualization • Plotting in Pandas vs Matplotlib • Bar plots • Histograms • Box plots • Grouped plots • Scatterplots • Trellis plots
- 6. Statistical Data Modeling • Statistical modeling • Fitting data to probability distributions • Fitting regression models • Model selection • Bootstrapping
- 7. Data Loading, Storage, and File Formats • Indexing: Can treat one or more columns as the returned DataFrame, and whether to get column names from the file, the user, or not at all. • Type inference and data conversion: This includes the user-defined value conversions and custom list of missing value markers. • Datetime parsing: Includes combining capability, including combining date and time information spread over multiple columns into a single column in the result. • Iterating: Support for iterating over chunks of very large files. • Unclean data issues: Skipping rows or a footer, comments, or other minor things like numeric data with thousands separated by commas
- 8. Packages For Statistical Analysis • pandas >= 0.11.1 and its dependencies • NumPy >= 1.6.1 • matplotlib >= 1.0.0 • pytz • IPython >= 0.1.2 • pyzmq • Tornado • Optional: statsmodels, xlrd and openpyxl
- 9. NumPy Basics: Arrays and Vectorized Computation • Fast vectorized array operations for data munging and cleaning, subsetting and filtering, transformation, and any other kinds of computations • Common array algorithms like sorting, unique, and set operations • Efficient descriptive statistics and aggregating/summarizing data • Data alignment and relational data manipulations for merging and joining together heterogeneous data sets • Expressing conditional logic as array expressions instead of loops with if- elifelse branches • Group-wise data manipulations (aggregation, transformation, function application).
- 10. Scipy SciPy is a collection of packages addressing a number of different standard problem domains in scientific computing. • SciPy. Integrate: numerical integration routines and differential equation solvers • scipy.linalg: linear algebra routines and matrix decompositions extending beyond those provided in numpy.linalg • scipy.optimize: function optimizers (minimizers) and root finding algorithms • scipy.signal: signal processing tools • scipy.sparse: sparse matrices and sparse linear system solvers