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Commita2835a5

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more exercises with the baby dataset
1 parent9b70a04 commita2835a5

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16 files changed

+1016521
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‎.learn/assets/us_baby_names_right.csv

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‎.learn/exercises/05.6-iloc/README.md

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‎.learn/exercises/05.6-loc/README.md

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#Using loc function in Pandas
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You can also use the data_frame.loc function to filter records using logical operations as indexes, like this:
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```python
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# get people more than 18 years old
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data_frame.loc[data_frame['age']<18]
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```
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##📝 Instructions
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Using the loc function, print on the terminal all pokemons with an atack of more than 80
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##💻 Expected output
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```bash
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# Name Type 1 Type 2 HP Attack Defense Sp. Atk Sp. Def Speed Generation Legendary
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2 3 Venusaur Grass Poison 80 82 83 100 100 80 1 False
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3 3 VenusaurMega Venusaur Grass Poison 80 100 123 122 120 80 1 False
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6 6 Charizard Fire Flying 78 84 78 109 85 100 1 False
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7 6 CharizardMega Charizard X Fire Dragon 78 130 111 130 85 100 1 False
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8 6 CharizardMega Charizard Y Fire Flying 78 104 78 159 115 100 1 False
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.. ... ... ... ... .. ... ... ... ... ... ... ...
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795 719 Diancie Rock Fairy 50 100 150 100 150 50 6 True
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796 719 DiancieMega Diancie Rock Fairy 50 160 110 160 110 110 6 True
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797 720 HoopaHoopa Confined Psychic Ghost 80 110 60 150 130 70 6 True
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798 720 HoopaHoopa Unbound Psychic Dark 80 160 60 170 130 80 6 True
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799 721 Volcanion Fire Water 80 110 120 130 90 70 6 True
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```
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importpandasaspd
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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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print(data_frame.loc[data_frame['Attack']>80])
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#Filter and cound
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How many pokemos are legendary?
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Hint: use the loc function with a logica operation as index, use the`len` function to count.
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##💻 Expected output
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`65`
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importpandasaspd
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data_frame=pd.read_csv('.learn/assets/pokemon_data.csv')
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print(len(data_frame.loc[data_frame['Legendary']==True]))

‎.learn/exercises/05.7-iterate-dataframe/README.md

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#Clean up Baby Names dataset
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Let's start a new clean exercise to clean up a publicly know datasate of[US baby names](https://www.kaggle.com/kaggle/us-baby-names) from Kaggle.
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##📝 Instructions
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First lets understand the dataset by printing the first 10 rows
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- Import the`./assets/us_baby_names_right.csv` dataset.
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- Print the first 5 records on the command line.
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##💻 Expected output
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```bash
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Unnamed: 0 Id Name Year Gender State Count
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0 11349 11350 Emma 2004 F AK 62
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1 11350 11351 Madison 2004 F AK 48
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2 11351 11352 Hannah 2004 F AK 46
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3 11352 11353 Grace 2004 F AK 44
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4 11353 11354 Emily 2004 F AK 41
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```
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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.head())
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#Remove column
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We can see that the datasate first column is called "Unnamed: 0" and it contains a number that we don't know what it is.
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##📝 Instructions
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- Remove the first column from the dataset.
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- Print the first 5 records again.
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##💻 Expected output
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```bash
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Id Name Year Gender State Count
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0 11350 Emma 2004 F AK 62
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1 11351 Madison 2004 F AK 48
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2 11352 Hannah 2004 F AK 46
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3 11353 Grace 2004 F AK 44
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4 11354 Emily 2004 F AK 41
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```
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##Dataframe value_counts
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Are there more male or female names in the dataset?
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Hint: use the value_counts function to get the`Gender` value count.
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##💻 Expected output
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```bash
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F 558846
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M 457549
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Name: Gender, dtype: int64
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```
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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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count=data_frame['Gender'].value_counts()
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print(count)
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#Group By
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How many different names exist in the dataset?
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1. Use the dataframe.`groupby` function to group your table by name.
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2. Use the .`sum()` function available after you group by to cound the number of gruped occurences.
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3. Use the`len(result)` function to count the number of groups
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##💻 Expected output
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`17632`
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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").sum()
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print(len(names))

‎.learn/vscode_queue.json

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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"}]
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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"},{"name":"start_exercise","time":447177537931.47,"data":"04.1-from_dict"},{"name":"start_exercise","time":447180555960.543,"data":"04-Data_Frame"},{"name":"connection_ended","time":453841679092.838}]

‎app.py

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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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data_frame=pd.read_csv('.learn/assets/us_baby_names_right.csv')
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names=data_frame.groupby("Name").sum()
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print(len(names))

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