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(assignment01)=

# Assignment #1 (demo). Exploratory data analysis with Pandas

```{figure} /_static/img/ods_stickers.jpg
:name: ods_stickers
```

**<center>[mlcourse.ai](https://mlcourse.ai) – Open Machine Learning Course** </center><br>

Author: [Yury Kashnitsky](https://www.linkedin.com/in/kashnitskiy/). Translated and edited by [Sergey Isaev](https://www.linkedin.com/in/isvforall/), [Artem Trunov](https://www.linkedin.com/in/datamove/), [Anastasia Manokhina](https://www.linkedin.com/in/anastasiiamanokhina/), and [Yuanyuan Pao](https://www.linkedin.com/in/yuanyuanpao/). All content is distributed under the [Creative Commons CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license.


**Same assignment as a [Kaggle Kernel](https://www.kaggle.com/kashnitsky/a1-demo-pandas-and-uci-adult-dataset) + [solution](https://www.kaggle.com/kashnitsky/a1-demo-pandas-and-uci-adult-dataset-solution).**

**In this task you should use Pandas to answer a few questions about the [Adult](https://archive.ics.uci.edu/ml/datasets/Adult) dataset. (You don't have to download the data – it's already  in the repository). Choose the answers in the [web-form](https://docs.google.com/forms/d/1uY7MpI2trKx6FLWZte0uVh3ULV4Cm_tDud0VDFGCOKg).**

Unique values of features (for more information please see the link above):
- `age`: continuous;
- `workclass`: `Private`, `Self-emp-not-inc`, `Self-emp-inc`, `Federal-gov`, `Local-gov`, `State-gov`, `Without-pay`, `Never-worked`;
- `fnlwgt`: continuous;
- `education`: `Bachelors`, `Some-college`, `11th`, `HS-grad`, `Prof-school`, `Assoc-acdm`, `Assoc-voc`, `9th`, `7th-8th`, `12th`, `Masters`, `1st-4th`, `10th`, `Doctorate`, `5th-6th`, `Preschool`;
- `education-num`: continuous;
- `marital-status`: `Married-civ-spouse`, `Divorced`, `Never-married`, `Separated`, `Widowed`, `Married-spouse-absent`, `Married-AF-spouse`,
- `occupation`: `Tech-support`, `Craft-repair`, `Other-service`, `Sales`, `Exec-managerial`, `Prof-specialty`, `Handlers-cleaners`, `Machine-op-inspct`, `Adm-clerical`, `Farming-fishing`, `Transport-moving`, `Priv-house-serv`, `Protective-serv`, `Armed-Forces`;
- `relationship`: `Wife`, `Own-child`, `Husband`, `Not-in-family`, `Other-relative`, `Unmarried`;
- `race`: `White`, `Asian-Pac-Islander`, `Amer-Indian-Eskimo`, `Other`, `Black`;
- `sex`: `Female`, `Male`;
- `capital-gain`: continuous.
- `capital-loss`: continuous.
- `hours-per-week`: continuous.
- `native-country`: `United-States`, `Cambodia`, `England`, `Puerto-Rico`, `Canada`, `Germany`, `Outlying-US(Guam-USVI-etc)`, `India`, `Japan`, `Greece`, `South`, `China`, `Cuba`, `Iran`, `Honduras`, `Philippines`, `Italy`, `Poland`, `Jamaica`, `Vietnam`, `Mexico`, `Portugal`, `Ireland`, `France`, `Dominican-Republic`, `Laos`, `Ecuador`, `Taiwan`, `Haiti`, `Columbia`, `Hungary`, `Guatemala`, `Nicaragua`, `Scotland`, `Thailand`, `Yugoslavia`, `El-Salvador`, `Trinadad&Tobago`, `Peru`, `Hong`, `Holand-Netherlands`;
- `salary`: `>50K`, `<=50K`.


```{code-cell} ipython3
import numpy as np
import pandas as pd

pd.set_option("display.max.columns", 100)
# to draw pictures in jupyter notebook
%matplotlib inline
# we don't like warnings
# you can comment the following 2 lines if you'd like to
import warnings

import matplotlib.pyplot as plt
import seaborn as sns

warnings.filterwarnings("ignore")
```


```{code-cell} ipython3
# for Jupyter-book, we copy data from GitHub, locally, to save Internet traffic,
# you can specify the data/ folder from the root of your cloned
# https://github.com/Yorko/mlcourse.ai repo, to save Internet traffic
DATA_URL = "https://raw.githubusercontent.com/Yorko/mlcourse.ai/main/data/"
```


```{code-cell} ipython3
data = pd.read_csv(DATA_URL + "adult.data.csv")
data.head()
```

**1. How many men and women (*sex* feature) are represented in this dataset?**


```{code-cell} ipython3
# You code here (read-only in a JupyterBook, pls run jupyter-notebook to edit)
```

**2. What is the average age (*age* feature) of women?**


```{code-cell} ipython3
# You code here (read-only in a JupyterBook, pls run jupyter-notebook to edit)
```

**3. What is the percentage of German citizens (*native-country* feature)?**


```{code-cell} ipython3
# You code here (read-only in a JupyterBook, pls run jupyter-notebook to edit)
```

**4-5. What are the mean and standard deviation of age for those who earn more than 50K per year (*salary* feature) and those who earn less than 50K per year?**


```{code-cell} ipython3
# You code here (read-only in a JupyterBook, pls run jupyter-notebook to edit)
```

**6. Is it true that people who earn more than 50K have at least high school education? (*education* – `Bachelors`, `Prof-school`, `Assoc-acdm`, `Assoc-voc`, `Masters` or `Doctorate` feature)**


```{code-cell} ipython3
# You code here (read-only in a JupyterBook, pls run jupyter-notebook to edit)
```

**7. Display age statistics for each race (*race* feature) and each gender (*sex* feature). Use *groupby()* and *describe()*. Find the maximum age of men of `Amer-Indian-Eskimo` race.**


```{code-cell} ipython3
# You code here (read-only in a JupyterBook, pls run jupyter-notebook to edit)
```

**8. Among whom is the proportion of those who earn a lot (`>50K`) greater: married or single men (*marital-status* feature)? Consider as married those who have a *marital-status* starting with *Married* (`Married-civ-spouse`, `Married-spouse-absent` or `Married-AF-spouse`), the rest are considered bachelors.**


```{code-cell} ipython3
# You code here (read-only in a JupyterBook, pls run jupyter-notebook to edit)
```

**9. What is the maximum number of hours a person works per week (*hours-per-week* feature)? How many people work such a number of hours, and what is the percentage of those who earn a lot (`>50K`) among them?**


```{code-cell} ipython3
# You code here (read-only in a JupyterBook, pls run jupyter-notebook to edit)
```

**10. Count the average time of work (*hours-per-week*) for those who earn a little and a lot (*salary*) for each country (*native-country*). What will these be for Japan?**


```{code-cell} ipython3
# You code here (read-only in a JupyterBook, pls run jupyter-notebook to edit)
```