CLOUD ACADEMY WORKING WITH PANDAS-STM

                             .:PROUDLY PRESENTS:. 
                        Cloud Academy Working with PANDAS                       
                          Release Date.: 22-06-2020 
                          Type.: Bookware 
                          Disks.: 38x15mb     
                          Link.: https://cloudacademy.com     
                       Release Notes         
       What is PandasPandas is Pythons ETL package for structured
       dataBuilt on top of numpy, designed to mimic the
       functionality of R dataframesProvides a convenient way to
       handle tabular dataCan perform all SQL functionalities,
       including group-by and join.Compatible with many other Data
       Science packages, including visualisation packages such as
       Matplotlib and SeabornDefines two main data
       types:pandas.Seriespandas.DataFrameSeriesGeneralised array -
       -- can be viewed as a table with a single columnIt consists
       of two numpy arrays:Index array: stores the index of the
       elementsvalues array: stores the values of the elementsEach
       array element has an unique index (ID), contained in a
       separate index arrayIf we reorder the series, the index
       moves with element. So an index will always identify with
       the same element in the seriesIndices do not have to be
       sequential, they do not even have to be numbers.Think
       indices as the primary keys for each row in a single column
       tableDataFramesA pandas DataFrame represents a table, it
       containsData in form of rows and columnsRow IDs (the index
       array, i.e. primary key)Column names (ID of the columns)A
       DataFrame is equivalent to collection of Series with each
       Series representing a columnThe row indices by default
       start from 0 and increase by one for each subsequent row,
       but just like Series they can be changed to any collection
       of objectsEach row index uniquely identifies a particular
       row. If we reorder the rows, their indices go with
       themGroup ByGroups are usually used together with
       reductionsCounting number of rows in each
       groupmy_dataframe.groupby(criteria).size()Sum of every
       numerical column in each
       groupmy_dataframe.groupby(criteria).sum()Mean of every
       numerical column in each
       groupmy_dataframe.groupby(criteria).mean()JoinUse
       DataFrame.merge() as a general method of joining two
       dataframes:Works also with seriesJoins on the primary keys
       of the two dataframes (series)Missing ValuesFinding out
       number of missing values in each
       columnmy_dataframe.isna().sum()Removing
       rowsmy_dataframe.dropna(axis = 0)Removing
       columnsmy_dataframe.dropna(axis = 1)Filling with a valueFor
       all missing values:
       my_dataframe.fillna(replacement_value)Different value for
       each column: my_dataframe.fillna({NAME: UNKNOWN, AGE:
       0}) Map, Replace, ApplyMap applies a mapping to every
       element of the dataframemy_dataframe.map({old1: new1, old2:
       new2, })my_dataframe.map(function)If we provide map using
       a dictionary, then any elements not in the keys will be
       mapped to numpy.nanReplace applies a mapping to only
       elements of the dataframe that have been mentioned in the
       mappingmy_dataframe.replace ({old1: new1, old2: new2,
       })Any elements not in the dictionary keys will not be
       changed
                       Greetings fly out to:            
                     Kodemusen, KoseBamsen           
                         STM is back.                
                For all the ppl we worked with        
                in the past. We salute you.          
                     NFO by NiMiTech              
                  Updated: 09/09/2002          

541M	Cloud Academy Working with PANDAS
541M	Cloud Academy Working with PANDAS/01.Working With PANDAS
83M	Cloud Academy Working with PANDAS/01.Working With PANDAS/01.01.PANDAS - Introduction.mp4
24K	Cloud Academy Working with PANDAS/01.Working With PANDAS/01.01.PANDAS - Introduction.vtt
47M	Cloud Academy Working with PANDAS/01.Working With PANDAS/01.02.PANDAS -  Arrays.mp4
12K	Cloud Academy Working with PANDAS/01.Working With PANDAS/01.02.PANDAS -  Arrays.vtt
35M	Cloud Academy Working with PANDAS/01.Working With PANDAS/01.03.PANDAS - Working With Queries.mp4
8.0K	Cloud Academy Working with PANDAS/01.Working With PANDAS/01.03.PANDAS - Working With Queries.vtt
202M	Cloud Academy Working with PANDAS/01.Working With PANDAS/01.04.PANDAS - Working With Data Frames.mp4
32K	Cloud Academy Working with PANDAS/01.Working With PANDAS/01.04.PANDAS - Working With Data Frames.vtt
66M	Cloud Academy Working with PANDAS/01.Working With PANDAS/01.05.PANDAS - Using GROUP BY.mp4
110M	Cloud Academy Working with PANDAS/01.Working With PANDAS/01.06.PANDAS - Merging and Joining.mp4
541M	total