Basically, I don't give any description but I want to tell you that I made this PPT with my crush. That's why it is my first PPT which I can upload on slide share.

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

11. REFERENCES
http://oreilly.com/catalog/errata.csp?isbn=9781449319793f
corporate@oreilly.com
http://oreil.ly/python_for_data_analysis
http://facebook.com/oreilly
http://twitter.com/oreillymedia
http://www.youtube.com/oreillymedia