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CLOUD ACADEMY WORKING WITH PANDAS-STM | Apps-Tutorials | MP4,VTT | 540.80 MiB
925 kb/s 1920×1080 | AAC 160 kb/s 2 CH eng
.: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
File: 01.01.PANDAS - Introduction.mp4 Size: 86742146 bytes (82.72 MiB), duration: 00:12:29, avg.bitrate: 926 kb/s Audio: aac, 44100 Hz, stereo (eng) Video: h264, yuv420p, 1920x1080, 29.97 fps(r) (eng)
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