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Commit9b70a04

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added exercies about series
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‎.learn/assets/series_dataframe.png

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‎.learn/exercises/03-Dataset/README.md

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Possible datasets based on your objective prediccion:
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- If sell shoes, how much shoes do I need in my wharehouse for the next month? A good dataset will be sales from the past 2 years.
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- Ifwesell shoes, how much shoes do I need in my wharehouse for the next month? A good dataset will be sales from the past 2 years.
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- I'm we are building a program to diagnost neumonia, a good dataset will be 100 xrays with neumonia and 100 x-rays without neumonia.
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You have to be creative when creating your datasets, think about all the variables that affect a predicion and try collecting and organizing everthing in one or many datasets.

‎.learn/exercises/04-Series/README.md

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#Series
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A series is similar to an array or list, it's a one dimentional data structure.
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You can create a series like this:
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```py
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data= pd.Series([1,94,85,31,23,8])
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```
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The result of printing`data` on the terminal will be something like this:
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```shell
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0 1
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1 94
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2 85
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3 31
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4 23
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5 8
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dtype: int64
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```
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##📝 Instructions
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Create a series from this list:`ages = [23,45,7,34,6,63,36,78,54,34]`
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##💻 Expected output
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```
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0 23
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1 45
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2 7
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3 34
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4 6
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5 63
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6 36
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7 78
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8 54
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9 34
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dtype: int64
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```
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importpandasaspd
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# two dimensional array of name,age values.
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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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#Series Date Range
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Use the pd.date_range function to create a series from`05-01-2021` to`05-12-2021`
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##💻 Expected output
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```
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DatetimeIndex(['2021-05-01', '2021-05-02', '2021-05-03', '2021-05-04',
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'2021-05-05', '2021-05-06', '2021-05-07', '2021-05-08',
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'2021-05-09', '2021-05-10', '2021-05-11', '2021-05-12'],
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dtype='datetime64[ns]', freq='D')
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```
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importpandasaspd
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date_series=pd.date_range(start='05-01-2021',end='05-12-2021')
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print(date_series)
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#Series Apply
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Given a series variable like:
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```py
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my_series= pd.Series([2,4,6,8,10])
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```
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Use the function my_series.apply to divide all numbers on the following series by 2.
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##💻 Expected output
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```bash
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0 1.0
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1 2.0
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2 3.0
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3 4.0
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4 5.0
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dtype: float64
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```
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importpandasaspd
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my_series=pd.Series([2,4,6,8,10])
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modified_series=my_series.apply(lambdax:x/2)
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print(modified_series)

‎.learn/exercises/04.1-iloc/README.mdrenamed to‎.learn/exercises/05.2-iloc/README.md

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#DataFrame iLoc
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The iloc
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The iloc command lets you retrieve any cel in your data_frame, for example to print the cel in the`x-axis: 2` and`y-axis: 4` we would have to type the following:
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```python
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# Print only item in the Row: 2, Col: 1

‎.learn/exercises/04.4-print-columns/README.mdrenamed to‎.learn/exercises/05.5-print-columns/README.md

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#Print columns
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You can also print jsut one collumn of your choice, for example:
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You can also print just one collumn of your choice, for example:
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```python
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print(data_frame['Type 1'])#this will print only the column "Type 1"
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```
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You can also use the square brackets`[]` to pick the range of rows you want to print like this:
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```python

‎.learn/exercises/04.5-iloc/README.mdrenamed to‎.learn/exercises/05.6-iloc/README.md

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#`08`Using the iloc function in Pandas
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#Using the iloc function in Pandas
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Pandas has a way to retrieve values by specifying the row and column number, for example: 2,4 (row 2, column 4).
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‎.learn/vscode_queue.json

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[{"name":"initializing","time":101570628280.769},{"name":"reset","time":101570628350.183},{"name":"configuration_loaded","time":101571646051.985}]
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[{"name":"initializing","time":442957258393.204},{"name":"reset","time":442957258439.738},{"name":"configuration_loaded","time":442958277103.692},{"name":"start_exercise","time":443015519309.933,"data":"00-welcome"},{"name":"start_exercise","time":443060088697.605,"data":"01-terminal"},{"name":"start_exercise","time":443074953846.711,"data":"01.2-Pipenv"},{"name":"start_exercise","time":443118267987.033,"data":"02-installation"},{"name":"start_exercise","time":443140770208.037,"data":"02.2-create-script"},{"name":"start_exercise","time":443206131153.638,"data":"02.3-import_pandas"},{"name":"start_exercise","time":443230227006.7,"data":"03-Dataset"},{"name":"start_exercise","time":443294961210.749,"data":"04-Data_Frame"},{"name":"start_exercise","time":443317656762.219,"data":"04.1-from_dict"},{"name":"start_exercise","time":443330217152.771,"data":"04.1-iloc"},{"name":"start_exercise","time":443400861084.733,"data":"04.2-head"},{"name":"start_exercise","time":443415968058.695,"data":"04.3-tail"},{"name":"start_exercise","time":443420883130.11,"data":"04.4-print-columns"},{"name":"start_exercise","time":443468150689.343,"data":"04.5-iloc"},{"name":"start_exercise","time":443588107863.337,"data":"04-Data_Frame"}]

‎app.py

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importpandasaspd
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# two dimensional array of name,age values.
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data= [['Alex',10],['Bob',12],['Clarke',13]]
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# create the dataframe and name the columns
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df=pd.DataFrame(data,columns=['Name','Age'])
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date_series=pd.date_range(start='05-01-2021',end='05-12-2021')
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# print the dataframe
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print(df)
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print(date_series)

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