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Commitf95063a

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‎.learn/telemetry.json

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‎.learn/vscode_queue.json

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[{"name":"initializing","time":9547038.8},{"name":"reset","time":9547149.9},{"name":"configuration_loaded","time":9558674.6},{"name":"start_exercise","time":10250066.1,"data":"02-install"},{"name":"start_exercise","time":10133553.2,"data":"02.2-import"},{"name":"start_exercise","time":10058047,"data":"03-datasets"},{"name":"start_exercise","time":10242720.7,"data":"04-Series"},{"name":"start_exercise","time":9982190.3,"data":"04.1-date-range"},{"name":"start_exercise","time":9925573.2,"data":"04.2-series-apply"},{"name":"start_exercise","time":10086186.7,"data":"05-dataframes"},{"name":"start_exercise","time":10549237.9,"data":"05.1-dataframe-dict"},{"name":"start_exercise","time":10313961,"data":"05.2-dataframe-iloc"},{"name":"start_exercise","time":10597356,"data":"05.3-dataframe-head"},{"name":"start_exercise","time":11046702,"data":"05.4-dataframe-tail"},{"name":"start_exercise","time":11351239.7,"data":"05.5-print-columns"},{"name":"start_exercise","time":11227500.4,"data":"05.6-loc-function"},{"name":"start_exercise","time":11824563.6,"data":"05.7-filter-and-count"},{"name":"open_terminal","time":11416015.8,"data":""},{"name":"start_exercise","time":11303766.9,"data":"06-clean-datasets"},{"name":"start_exercise","time":11680878.6,"data":"06.1-remove-column"},{"name":"start_exercise","time":11825870,"data":"06.2-value-counts"},{"name":"start_exercise","time":12225704,"data":"06.3-group-by"}]

‎Pipfile

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[[source]]
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url ="https://pypi.org/simple"
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verify_ssl =true
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name ="pypi"
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[packages]
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pandas ="*"
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[dev-packages]
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[requires]
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python_version ="3.10"

‎Pipfile.lock

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‎app.py

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importpandasaspd
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# Read data from a csv
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data_frame=pd.read_csv('.learn/assets/pokemon_data.csv')
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print(data_frame)
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# Series
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data=pd.Series([23,45,7,34,6,63,36,78,54,34])
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print(data)
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# Date Range
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date_range=pd.date_range('2021-05-01','2021-05-12')
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print(date_range)
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# Series Apply
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defdivide(x):
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returnx/2
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my_series=pd.Series([2,4,6,8,10])
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print(my_series.apply(divide))
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# DataFrames from list
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data= [["Toyota","Corolla","Blue"], ["Ford","K","Yellow"], ["Porsche","Cayenne","White"]]
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df=pd.DataFrame(data,columns=['Brand','Model','Color'])
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print(df)
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# DataFrames from dict
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data_list_dict=data= [
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{
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"brand":"Toyota",
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"model":"Corolla",
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"color":"Blue"
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},
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{
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"brand":"Ford",
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"model":"K",
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"color":"Yellow"
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},
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{
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"brand":"Porsche",
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"model":"Cayenne",
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"color":"White"
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},
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{
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"brand":"Tesla",
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"model":"Model S",
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"color":"Red"
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}
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]
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df=pd.DataFrame(data_list_dict)
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print(df)
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# DataFrame iLoc
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print(data_frame.iloc[133,6])
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# DataFrame Head
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print(data_frame.head(3))
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# DataFrame Tail
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print(data_frame.tail(3))
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# Print Columns
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print(data_frame[['Name','Type 1']].head(10))
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# Loc Function
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print(data_frame.loc[data_frame['Attack']>80])
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#Filter and Count
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print(len(data_frame.loc[data_frame['Legendary']==True]))
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# Clean Datasets
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baby_names=pd.read_csv('.learn/assets/us_baby_names_right.csv')
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print(baby_names.head(5))
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# Remove Columns
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baby_names.drop(columns=baby_names.columns[0],axis=1,inplace=True)
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print(baby_names.head(5))
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# Value Counts
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print(baby_names.value_counts('Gender'))
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# Group By
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print(len(baby_names.groupby('Name').sum()))
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