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Commitb3304d6

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#pyhton-pandas-tutorial - all solutions
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‎.learn/telemetry.json

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‎.learn/vscode_queue.json

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[{"name":"initializing","time":176012449.799},{"name":"reset","time":176012474.769},{"name":"configuration_loaded","time":176020111.849},{"name":"start_exercise","time":175929465.262,"data":"00-welcome"},{"name":"start_exercise","time":176019876.799,"data":"01-new-terminal"},{"name":"start_exercise","time":176318772.484,"data":"01.2-pipenv"},{"name":"start_exercise","time":175482651.957,"data":"02-install"},{"name":"start_exercise","time":175665773.622,"data":"02.1-create-a-script"},{"name":"start_exercise","time":175802030.031,"data":"02.2-import"},{"name":"start_exercise","time":175972982.837,"data":"03-datasets"},{"name":"start_exercise","time":176133317.671,"data":"04-Series"},{"name":"start_exercise","time":176317415.677,"data":"04.1-date-range"},{"name":"start_exercise","time":175473421.687,"data":"04.2-series-apply"},{"name":"start_exercise","time":175636616.11,"data":"05-dataframes"},{"name":"start_exercise","time":175820587.455,"data":"05.1-dataframe-dict"},{"name":"start_exercise","time":175996817.127,"data":"05.2-dataframe-iloc"},{"name":"start_exercise","time":176162184.688,"data":"05.3-dataframe-head"},{"name":"start_exercise","time":175505153.744,"data":"05.4-dataframe-tail"},{"name":"start_exercise","time":175663634.159,"data":"05.5-print-columns"},{"name":"start_exercise","time":176083679.268,"data":"05.6-loc-function"},{"name":"start_exercise","time":176280161.041,"data":"05.7-filter-and-count"},{"name":"start_exercise","time":175651652.033,"data":"06-clean-datasets"},{"name":"start_exercise","time":175822790.663,"data":"06.1-remove-column"},{"name":"start_exercise","time":175965081.196,"data":"06.2-value-counts"},{"name":"open_terminal","time":175973706.728,"data":""},{"name":"start_exercise","time":175746885.05,"data":"06.1-remove-column"},{"name":"open_terminal","time":175993820.817,"data":""},{"name":"open_terminal","time":176440381.151,"data":""},{"name":"open_terminal","time":175953097.915,"data":""},{"name":"open_terminal","time":175651773.494,"data":""},{"name":"start_exercise","time":176287215.934,"data":"06.2-value-counts"},{"name":"start_exercise","time":176678109.822,"data":"06.3-group-by"},{"name":"open_terminal","time":176433481.766,"data":""}]

‎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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"""
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#02.1
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print("Hello World")
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#02.2
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import pandas as pd
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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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#04
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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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#04.1
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print(pd.date_range("2021-05-01", "2021-05-12"))
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#04.2
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def divide(n): return n / 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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#05 DataFrames
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data = [["Toyota", "Corolla", "Blue"], ["Ford", "K", "Yellow"], ["Porsche", "Cayenne", "White"]]
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data_frame = pd.DataFrame(data, columns=["Brand", "Model", "Color"])
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print(data_frame)
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#05.1 DataFrame Dict
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import pandas as pd
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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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data_frame = pd.DataFrame(data)
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new_row = ["Tesla", "Model S", "Red"]
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data_frame.loc[len(data_frame)] = new_row
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print(data_frame)
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#05.2 DA
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import pandas as pd
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data_frame = pd.read_csv(".learn/assets/pokemon_data.csv")
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print(data_frame.iloc[133, 6])
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#05.3 DataFrame Head
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print(data_frame.head(3))
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#05.4 DataFrame Tail
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import pandas as pd
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data_frame = pd.read_csv(".learn/assets/pokemon_data.csv")
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print(data_frame.tail(3))
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# 05.5 Print Columns
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import pandas as pd
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data_frame = pd.read_csv(".learn/assets/pokemon_data.csv")
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print(data_frame[['Name', 'Type 1']][0:10])
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# 05.6 Loc Function
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print(data_frame.loc[data_frame['Attack'] > 80])
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# 05.7 Filter and Count
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print(len(data_frame.loc[data_frame['Legendary'] == True]))
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import pandas as pd
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data_frame = pd.read_csv(".learn/assets/us_baby_names_right.csv")
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print(data_frame.head(5))
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#06.1 Remove Column
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import pandas as pd
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data_frame = pd.read_csv(".learn/assets/us_baby_names_right.csv")
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data_frame = data_frame.drop(labels = "Unnamed: 0", axis = 1)
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print(data_frame.head(5))
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"""
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#06.2 Value Counts
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importpandasaspd
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data_frame=pd.read_csv(".learn/assets/us_baby_names_right.csv")
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print(data_frame['Gender'].value_counts())
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#06.3 Group By
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importpandasaspd
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data_frame=pd.read_csv(".learn/assets/us_baby_names_right.csv")
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names=data_frame.groupby("Name")
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print(len(names.sum()))

‎conftest.py

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exceptAttributeError:
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metafunc.parametrize("app",[cached_app])
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if'configuration'inmetafunc.fixturenames:
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metafunc.parametrize("configuration", [json.loads('{"port":3000,"os":"linux","editor":{"mode":"extension","agent":"vscode","version":"3.1.44"},"dirPath":"./.learn","configPath":"learn.json","outputPath":".learn/dist","publicPath":"/preview","publicUrl":"https://cautious-space-sniffle-465wrg46xfqv47-3000.app.github.dev","contact":"https://github.com/learnpack/learnpack/issues/new","language":"auto","autoPlay":true,"projectType":"tutorial","grading":"incremental","exercisesPath":".learn/exercises","webpackTemplate":null,"disableGrading":false,"disabledActions":["build"],"actions":[],"entries":{"html":"index.html","vanillajs":"index.js","react":"app.jsx","node":"app.js","python3":"app.py","java":"app.java"},"preview":"https://github.com/4GeeksAcademy/python-pandas-tutorial/blob/main/.learn/assets/pandas-preview.jpeg?raw=true","difficulty":"beginner","duration":3,"description":{"us":"Master Pandas, the most popular Python library for machine learning, with our pandas tutorial exercises. Learn to create DataFrames, clean datasets, and more, with exercises developed by experts.","es":"Domina Pandas, la biblioteca más popular de Python para machine learning, con nuestro tutorial de python pandas. Aprende a crear DataFrames, limpiar datasets y más, con ejercicios desarrollados en 80 horas."},"technologies":["pandas","machine learning","data science","python"],"title":{"us":"Pandas tutorial exercises","es":"Tutorial de Pandas: Interactivo, auto-corregido y con mentor de inteligencia artificial"},"slug":"pandas-for-machine-learning","telemetry":{"batch":"https://breathecode.herokuapp.com/v1/assignment/me/telemetry"},"translations":[]}')])
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metafunc.parametrize("configuration", [json.loads('{"port":3000,"os":"linux","editor":{"mode":"extension","agent":"vscode","version":"4.0"},"dirPath":"./.learn","configPath":"learn.json","outputPath":".learn/dist","publicPath":"/preview","publicUrl":"http://localhost:3000","contact":"https://github.com/learnpack/learnpack/issues/new","language":"auto","autoPlay":true,"projectType":"tutorial","grading":"incremental","exercisesPath":".learn/exercises","webpackTemplate":null,"disableGrading":false,"disabledActions":["build"],"actions":[],"entries":{"html":"index.html","vanillajs":"index.js","react":"app.jsx","node":"app.js","python3":"app.py","java":"app.java"},"suggestions":{"agent":"vscode"},"warnings":{"agent":null},"preview":"https://github.com/4GeeksAcademy/python-pandas-tutorial/blob/main/.learn/assets/pandas-preview.jpeg?raw=true","difficulty":"beginner","duration":3,"description":{"us":"Master Pandas, the most popular Python library for machine learning, with our pandas tutorial exercises. Learn to create DataFrames, clean datasets, and more, with exercises developed by experts.","es":"Domina Pandas, la biblioteca más popular de Python para machine learning, con nuestro tutorial de python pandas. Aprende a crear DataFrames, limpiar datasets y más, con ejercicios desarrollados en 80 horas."},"technologies":["pandas","machine learning","data science","python"],"title":{"us":"Pandas tutorial exercises","es":"Tutorial de Pandas: Interactivo, auto-corregido y con mentor de inteligencia artificial"},"slug":"pandas-for-machine-learning","telemetry":{"batch":"https://breathecode.herokuapp.com/v1/assignment/me/telemetry"},"translations":[]}')])

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