#!/usr/bin/env python
# coding: utf-8

# ## Exploratory Data Analysis and Visualization

# The goal of __exploratory data analysis (EDA)__ is to explore attributes across multiple entities to decide what statistical or machine learning techniques to apply to the data. Visualizations are used to assist in understanding the data.

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# loads the pandas library 
import pandas as pd
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)  # Ignore Pandas future warnings

# creates data frame named df by reading in the Baltimore csv
df = pd.read_csv("manipulated_baltimore_data.csv")
df.head(n=3)


# The `.describe()` function summarizes a data frame column. Since the data type of `max_building_age` is currently type 'object', which in python is an indcator of type 'string', we have to first convert this attribute into a numeric value.

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df['max_building_age'].describe()


# Now that `max_building_age` is numeric type, we see that `describe()` provides __summary statistics__ on this attribute.

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# converts max_building age to numeric type
df["max_building_age"] = pd.to_numeric(df["max_building_age"])
df['max_building_age'].describe()


# We can the same operations to `max_annual_income`.

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df['max_annual_income'].describe()


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df['max_annual_income'] = pd.to_numeric(df['max_annual_income'])
df['max_annual_income'].describe()


# Finally we create some plots our data. A __scatter plot__ and a __bar chart__ are shown below.

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get_ipython().run_cell_magic('HTML', '', "<script type='text/javascript' src='https://10ay.online.tableau.com/javascripts/api/viz_v1.js'></script><div class='tableauPlaceholder' style='width: 1920px; height: 895px;'><object class='tableauViz' width='1920' height='895' style='display:none;'><param name='host_url' value='https%3A%2F%2F10ay.online.tableau.com%2F' /> <param name='embed_code_version' value='3' /> <param name='site_root' value='&#47;t&#47;sadata' /><param name='name' value='mapping_inequality&#47;Sheet1' /><param name='tabs' value='no' /><param name='toolbar' value='yes' /><param name='showAppBanner' value='false' /><param name='filter' value='iframeSizedToWindow=true' /></object></div>\n")


# ### Exercise 4

# > 1. Hover over different points and explore their additional characteristics. __Note__:`INHABITANTS_F/N` should be multiplied by 100 to be a percent.
# 2. The different points are clustered by grades. Which clusters have the most variation?
# 3. How does `BUILDINGS_Construction` vary across the different points?
# 4. Can you identify a trend overall? 

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get_ipython().run_cell_magic('HTML', '', "<script type='text/javascript' src='https://public.tableau.com/javascripts/api/tableau-2.min.js'></script><div class='tableauPlaceholder' style='width: 1440px; height: 715px;'><object class='tableauViz' width='1440' height='715' style='display:none;'><param name='host_url' value='https%3A%2F%2F10ay.online.tableau.com%2F' /> <param name='embed_code_version' value='3' /> <param name='site_root' value='&#47;t&#47;sadata' /><param name='name' value='mapping_inequality&#47;Sheet3' /><param name='tabs' value='no' /><param name='toolbar' value='yes' /><param name='showAppBanner' value='false' /><param name='filter' value='iframeSizedToWindow=true' /></object></div>\n")


# ### Excercise 5

# > 1. Recall the preperations done to the INHABITANTS_Foreignborn, how might this have influenced these outcomes?
# 2. What can you learn from this graph?
# 3. What do you learn about the different grades?

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get_ipython().run_cell_magic('HTML', '', "<script type='text/javascript' src='https://10ay.online.tableau.com/javascripts/api/viz_v1.js'></script><div class='tableauPlaceholder' style='width: 1440px; height: 715px;'><object class='tableauViz' width='1440' height='715' style='display:none;'><param name='host_url' value='https%3A%2F%2F10ay.online.tableau.com%2F' /> <param name='embed_code_version' value='3' /> <param name='site_root' value='&#47;t&#47;sadata' /><param name='name' value='mapping_inequality&#47;Sheet2' /><param name='tabs' value='no' /><param name='toolbar' value='yes' /><param name='showAppBanner' value='false' /><param name='filter' value='iframeSizedToWindow=true' /></object></div>\n")


# ### Exercise 6

# > 1. What can you learn from this graph?
# 2. What are some explanations for the outcomes?
# 3. What can you learn about the different grades?
# 4. Compare to the previous graph, what are the similarities and differences?

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