Downloaded 43 times
Uploaded byzekeLabs Technologies
Pandas
The document provides an agenda for a Pandas workshop covering data wrangling, visualization, and statistical modeling using Pandas. The agenda includes introductions to Pandas fundamentals like Series and DataFrames, data importing and exploration, missing data handling, reshaping data through pivoting and stacking, merging datasets, and grouping and computation. Later sections cover plotting and visualization, as well as statistical modeling techniques like linear models, time series analysis and Bayesian models. The workshop aims to simplify learning and teach how to use Pandas for data preparation, analysis and modeling.
More Related Content
![Introduction to Pandas and Time Series Analysis [PyCon DE]](https://cdn.slidesharecdn.com/ss_thumbnails/introductiontopandasandtimeseriesanalysispyconde-170617163724-thumbnail.jpg?width=640&height=640&fit=bounds)
![Introduction to Data Analtics with Pandas [PyCon Cz]](https://cdn.slidesharecdn.com/ss_thumbnails/introductiontodataanalticswithpandaspyconcz-170617163446-thumbnail.jpg?width=640&height=640&fit=bounds)
More from zekeLabs Technologies
![[Webinar] Following the Agile Footprint - zekeLabs](https://cdn.slidesharecdn.com/ss_thumbnails/followingtheagilefootprint-webinar-200130092825-thumbnail.jpg?width=640&height=640&fit=bounds)
Pandas
- 1. zekeLabs Pandas for Data Wrangling& Statistical Modeling Learning made Simpler ! www.zekeLabs.com
- 2. Agenda ● Introduction toPandas ● Data Wrangling with Pandas ● Plotting & Visualization ● Statistical Data Modeling
- 3. Introduction to Pandas ● Introduction toPandas ● Series and DataFrame objects ● Importing data ● Indexing, data selection and subsetting ● Hierarchical indexing ● Reading and writing files ● Date/time types ● String Operations ● Missing data ● Data summarization
- 4. Introduction to Pandas ●Open Source, High Performance, Easy-to-use data structure. ● Library for Data munging, preparation, analysis & modeling. ● Alternative to excel sheet ● Handle Time Series data ● Reads from different data formats ● Mutable in contents & size ● IO Tools to load from flat files, HDF5 etc.
- 5. Series ● Single vectorof data like NumPy but with index ● Imagine series as one column of table
- 6.
- 7. DataFrames ● Data Structureto store, view, manipulate multivariate data ● Tabular data structure ● Series represent univariate data ● Combine different series and create a dataframe
- 8. DataFrames ● Data Structureto store, view, manipulate multivariate data ● Tabular data structure ● Series represent univariate data ● Combine different series and create a dataframe
- 9. DataFrames - Creationfrom Series
- 10.
- 11. Importing data ● Datashould be loaded before anything could be done on it.
- 12.
- 13.
- 14.
- 15.
- 16.
- 17.
- 18.
- 19. Access Rows ByIndex & Index-Values
- 20. Access Rows ByIndex & Index-Values
- 21.
- 22.
- 23.
- 24. Handling Missing Values ●Datasets in real world will have missing values ● If only few values of a column is present, we might drop the entire column ● If only few rows are missing, we might drop the rows ● We can’t afford to dropping lot of rows, it like reducing the dataset.
- 25. Handling Missing Values ●Fillna - Filling missing values
- 26. Handling Missing Values ●dropna - dropping based on missing values
- 27. Handling Missing Values ●replace - Replace values given in ‘to_replace’ with ‘value’.
- 28.
- 29.
- 30. JSON Normalizing ● Handlingsemi-structured data
- 31. Working with TextData ● Series with string data have ‘.str’ as a module. ● Inside .str we have many string utility functions.
- 32. Working with TextData - 2
- 33. Working with TextData - 3
- 34. Handling DateTime data ●Time series data appears very often in datasets ● Loading columns as datetime when loading file ● Parsing Datetimes ● Display datetime with time zones ● Rounding datetimes ● Filtering data between certain datetime ● Creating ranges
- 35.
- 36.
- 37.
- 38.
- 39.
- 40.
- 41.
- 42. Data Wrangling with Pandas ● ReshapingDataFrame objects ● Pivoting ● Alignment ● Data aggregation and GroupBy operations ● Merging and joining DataFrame objects
- 43. GroupBy : split-apply-combine Splittingdata into groups based on certain criteria Applying a function to each group independently ● Aggregate ● Transform ● Filtering Combining the results into a data structure
- 44. GroupBy : splittingan object into groups
- 45.
- 46.
- 47.
- 48.
- 49.
- 50.
- 51.
- 52.
- 53.
- 54.
- 55.
- 56.
- 57. Computation Splitting data intogroups based on certain criteria Applying a function to each group independently ● Aggregate ● Transform ● Filtering Combining the results into a data structure
- 58. Computation Tool ● StatisticalFunctions ● Window Functions ● Aggregations
- 59. Plotting & Visualization ● Introduction ofMatplotlib ● Time series plots ● Grouped plots ● Scatterplots ● Histograms ● Box-plot ● Pie Charts
- 60.
- 61. Statistical Data Modeling ● Fittingdata to probability distributions ● Linear models ● Spline models ● Time series analysis ● Bayesian models
- 63.
- 64. Visit : www.zekeLabs.comfor more details THANK YOU Let us know how can we help your organization to Upskill the employees to stay updated in the ever-evolving IT Industry. Get in touch: www.zekeLabs.com | +91-8095465880 | info@zekeLabs.com