Pandas with Python for Data Science
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- Curriculum
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The goal of this course is to make the trainees expert on working with Pandas python libraries. This training will be helping folks to achieve proficiency in introducing the concept of data science with the help of libraries that we will be covering here. This course has been focused on training on Pandas. All the concepts that revolve around these libraries will be detailed very precisely through this course. The sole objective of this course is to enrich the trainees with the entire set of skills that are required to work with these python-based libraries. In this unit, you will get to learn about the basics of these libraries, what it can offer, and what kind of problems could be solved using these libraries. The initial hour in this unit has been given to explain the introduction while the rest of the time has been devoted to explaining the main concepts.
Pandas is an open-source, BSD-licensed Python library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. This Python course will get you up and running with using Python for data analysis and visualization. The training will include the following:
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Installing Jupyter
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Jupyter Environment
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Read data using Pandas
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Series vs Data Frame
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Basic Operations in Pandas
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Analyze the imported data
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Renaming Columns
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Sorting
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Filtering Data
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Filtering Function
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Read Selective Columns & Rows
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2Understanding Jupiter EnvironmentVideo lesson
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3Reading the Data SetVideo lesson
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4Series and Data FrameVideo lesson
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5Operations in Data SetVideo lesson
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6More on Panda FunctionsVideo lesson
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7Column Names and OperationVideo lesson
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8Removing Columns and RowsVideo lesson
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9Sorting Data FrameVideo lesson
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10Filter Multiple CriteriaVideo lesson
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11Selective Columns and RowsVideo lesson
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12Data Frame and SeriesVideo lesson
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13Axis ParameterVideo lesson
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14String Methods in PandasVideo lesson
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15Changing the Data TypesVideo lesson
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16Example of Data Type ChangeVideo lesson
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17Group by FunctionsVideo lesson
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18Functions on SeriesVideo lesson
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19Plotting series in PandasVideo lesson
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20Dealing with Null ValuesVideo lesson
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21Uses of IndexVideo lesson
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22Column in IndexVideo lesson
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23Output of DataVideo lesson
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24Functions of iX MethodVideo lesson
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25InPlace ParameterVideo lesson
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26Inspecting the SpaceVideo lesson
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27Reducing the SpaceVideo lesson
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28Using in Country SeriesVideo lesson
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29Creating Manual Data FrameVideo lesson
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30Random Sampling with PandasVideo lesson
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31Concept of Dummy CodingVideo lesson
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32Creating Dummified ValuesVideo lesson
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33Duplicates in Data FrameVideo lesson
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34Functions for Date and TimeVideo lesson
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35Setting with Copy WarningVideo lesson
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36Example on Copy WarningVideo lesson
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37Changing the Display OptionVideo lesson
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38Formatting the DataVideo lesson
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39Tricks for Display OptionsVideo lesson
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40Data with Rows and ColumnsVideo lesson
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41Converting Data FrameVideo lesson
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42Introduction to Azure Data LakeVideo lesson
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43Merging Data FramesVideo lesson
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44Shaping a Data FrameVideo lesson
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45Filling NA ValuesVideo lesson
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46Importing Time Series DataVideo lesson
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47Working with Interpolate MethodVideo lesson
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48Stacking and UnstackingVideo lesson
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49Stacking and Unstacking for 3 LevelsVideo lesson
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50Concept of CrosstabVideo lesson
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51More on CrosstabVideo lesson
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52More Options with CrosstabVideo lesson
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53Functions of PivotVideo lesson
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54Pivot Table MethodVideo lesson
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55Example on Pivot TableVideo lesson
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56Data Frame to CSV FileVideo lesson
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57Using Excel FunctionsVideo lesson
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