Visualization

Plotting in Python¶

There are several popular plotting libaries:

• matplotlib: low level plotting tools

• seaborn: high level plotting with opinionated defaults

• ggplot: plotting based on the ggplot library in R.

Plus pandas has a plot method

Pandas and seaborn use matplotlib under the hood.

Seaborn and ggplot both assume the data is set up as a DataFrame. Getting started with seaborn is the simplest, so we’ll use that.

Figure and axis level plots¶

add the image to your notebook with the following:

![summary of plot types](https://seaborn.pydata.org/_images/function_overview_8_0.png)

Anatomy of a figure¶

*this was drawn with code

add the image to your notebook with the following:

![annotated graph](https://matplotlib.org/stable/_images/sphx_glr_anatomy_001.png)

figure vs axes

we will load pandas and seaborn

import pandas as pd
import seaborn as sns

and we will use a new dataset for today with more rows:

arabica_data_url = 'https://raw.githubusercontent.com/jldbc/coffee-quality-database/master/data/arabica_data_cleaned.csv'

then load it in as normal

coffee_df = pd.read_csv(arabica_data_url,index_col=0)

since it’s new, we will take a quick look with head

coffee_df.head()

and then see the shape to know how big it is before we start working

coffee_df.shape

coffee_df['Flavor'].describe()

Distribution Plots¶

sns.displot(data=coffee_df,x='Flavor')

<seaborn.axisgrid.FacetGrid at 0x7f244b708a70>

sns.displot(data=coffee_df,x='Flavor',kind='kde')

<seaborn.axisgrid.FacetGrid at 0x7f244ac61640>

sns.displot(data=coffee_df,x='Flavor',kind='kde',hue='Color')

<seaborn.axisgrid.FacetGrid at 0x7f244a3240e0>

Relation Plots¶

sns.relplot(data=coffee_df, x='Flavor',y='Balance')

<seaborn.axisgrid.FacetGrid at 0x7f244a053350>

sns.relplot(data=coffee_df, x='Flavor',y='Balance',hue='Color')

<seaborn.axisgrid.FacetGrid at 0x7f2447d0b350>

coffee_df.columns

sns.relplot(data=coffee_df, x='Flavor',y='Balance',hue='Color',col='Country.of.Origin',col_wrap=4)

/home/runner/.local/lib/python3.12/site-packages/seaborn/axisgrid.py:854: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
  func(*plot_args, **plot_kwargs)

/home/runner/.local/lib/python3.12/site-packages/seaborn/axisgrid.py:854: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
  func(*plot_args, **plot_kwargs)

<seaborn.axisgrid.FacetGrid at 0x7f2447d27440>

Categorical Plots¶

By default a catplot is a stripplot

sns.catplot(coffee_df, y='Number.of.Bags', x='Country.of.Origin')

<seaborn.axisgrid.FacetGrid at 0x7f24450d71a0>

For this data, this is really hard to understand, let’s change to a bar version

sns.catplot(coffee_df, y='Number.of.Bags', x='Country.of.Origin',kind='bar')

<seaborn.axisgrid.FacetGrid at 0x7f2447d255b0>

Here, it takes the mean for each country and that is the bar height and the line is the std for each country.

It uses logic similar to:

coffee_df.groupby('Country.of.Origin')['Number.of.Bags'].describe()

Filtering with isin¶

First we can find the top countries, we noticed that the value_counts are sorted so we take the first 10

top_countries = coffee_df['Country.of.Origin'].value_counts()[:10].index
top_countries

we can use that to filter the original DataFrame. To do this, we use isin to check each element in the 'Country.of.Origin' column is in that list.

coffee_df['Country.of.Origin'].isin(top_countries)

This is roughly equivalent to:

[country in top_countries for country in coffee_df['Country.of.Origin'] ]

except this builds a list and the pandas way makes a pd.Series object. The Python in operator is really helpful to know and pandas offers us an isin method to get that type of pattern.

In a more basic programming format this process would be two separate loops worth of work.

c_in = []
# iterate over the country of each rating
for country in coffee_df['Country.of.Origin']:
    # make a false temp value
    cur_search = False
    # iterate over top countries
    for tc in top_countries:
        # flip the value if the current top & rating cofee match
        if tc==country:
            cur_search = True
    # save the result of the search
    c_in.append(cur_search)

With that list of booleans, we can then mask the original DataFrame. This keeps only the value where the inner quantity is True

top_coffee_df = coffee_df[coffee_df['Country.of.Origin'].isin(top_countries)]
top_coffee_df.head(1)

And now we can plot from that new dataframe

sns.catplot(data =top_coffee_df,x='Country.of.Origin',y='Number.of.Bags', aspect=3)

<seaborn.axisgrid.FacetGrid at 0x7f2442b88bf0>

Now with the stripplot we can see that it puts a point for each coffee (row in the dataframe) and adds random jitter so they do not all overlap since the x values are the countries which are discrete

Install some extra tools¶

pip install jupytext mystmd

Variable types and data types¶

Related but not the same.

Data types are literal, related to the representation in the computer.

ther can be int16, int32, int64

We can also have mathematical types of numbers

• Integers can be positive, 0, or negative.

• Reals are continuous, infinite possibilities.

Variable types are about the meaning in a conceptual sense.

• categorical (can take a discrete number of values, could be used to group data, could be a string or integer; unordered)

• continuous (can take on any possible value, always a number)

• binary (like data type boolean, but could be represented as yes/no, true/false, or 1/0, could be categorical also, but often makes sense to calculate rates)

• ordinal (ordered, but appropriately categorical)

we’ll focus on the first two most of the time. Some values that are technically only integers range high enough that we treat them more like continuous most of the time.

Grading Review¶

We discussed the grading

Assignment 1 Q&A¶

For this assignment if you atted Friday office hours you can get an automatic extension, but going forward the policy will apply.

Questions After Class¶

Why did you use the . in between spaces for country of origin?¶

because that is how the column is actually named.

We are picking out the column name, not just describing it.

coffee_df.columns

If we pick any of those values we get that column:

from numpy.random import choice
random_col = choice(coffee_df.columns)
coffee_df[random_col].head()

or any other one

random_col = choice(coffee_df.columns)
coffee_df[random_col].head()

typo_col = 'country of origin'
coffee_df[typo_col].head()

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
File ~/.local/lib/python3.12/site-packages/pandas/core/indexes/base.py:3812, in Index.get_loc(self, key)
   3811 try:
-> 3812     return self._engine.get_loc(casted_key)
   3813 except KeyError as err:

File pandas/_libs/index.pyx:167, in pandas._libs.index.IndexEngine.get_loc()

File pandas/_libs/index.pyx:196, in pandas._libs.index.IndexEngine.get_loc()

File pandas/_libs/hashtable_class_helper.pxi:7088, in pandas._libs.hashtable.PyObjectHashTable.get_item()

File pandas/_libs/hashtable_class_helper.pxi:7096, in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'country of origin'

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
Cell In[26], line 2
      1 typo_col = 'country of origin'
----> 2 coffee_df[typo_col].head()

File ~/.local/lib/python3.12/site-packages/pandas/core/frame.py:4113, in DataFrame.__getitem__(self, key)
   4111 if self.columns.nlevels > 1:
   4112     return self._getitem_multilevel(key)
-> 4113 indexer = self.columns.get_loc(key)
   4114 if is_integer(indexer):
   4115     indexer = [indexer]

File ~/.local/lib/python3.12/site-packages/pandas/core/indexes/base.py:3819, in Index.get_loc(self, key)
   3814     if isinstance(casted_key, slice) or (
   3815         isinstance(casted_key, abc.Iterable)
   3816         and any(isinstance(x, slice) for x in casted_key)
   3817     ):
   3818         raise InvalidIndexError(key)
-> 3819     raise KeyError(key) from err
   3820 except TypeError:
   3821     # If we have a listlike key, _check_indexing_error will raise
   3822     #  InvalidIndexError. Otherwise we fall through and re-raise
   3823     #  the TypeError.
   3824     self._check_indexing_error(key)

KeyError: 'country of origin'

this is because country of origin is not a column in the dataset

typo_col in coffee_df.columns

is the random jitter for same number inclusive or exclude (can the dots overlap completely if unlucky)¶

it is random, so they can overlap, but it would be exceedingly unlucky for more than two to cmopletely overlap