day 4: getting started with data visualization¶
- overview day 4
- section 4.2.1 python syntax
- section 4.2.2 functions
- section 4.2.3 files
section 4.2.1¶
- seaborn-additional-regression-plots.ipynb
- seaborn-bar-plot-count-plot.ipynb
- seaborn-box-plot.ipynb
- seaborn-categorical-data-plot.ipynb
- seaborn-color-palettes.ipynb
- seaborn-controlling-aesthetics.ipynb
- seaborn-facetgrid.ipynb
- seaborn-factor-plot.ipynb
- seaborn-lm-plot-reg-plot.ipynb
- seaborn-loading-dataset.ipynb
- seaborn-matplotlib-vs-seaborn.ipynb
- seaborn-pairgrid.ipynb
- seaborn-scatter-plot-joint-plot.ipynb
- seaborn-strip-plot.ipynb
- seaborn-time-series-and-letter-value-plot.ipynb
- seaborn-violin-plot.ipynb
section 4.2.2¶
some line
section 4.2.3¶
some line
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#!pip install seaborn
import seaborn as sns
#%matplotlib inline
#%reload_ext autoreload
#%autoreload 2
#dfc = pd.read_csv('some.csv')
print(sns.get_dataset_names())
dfc = sns.load_dataset('car_crashes')
dfc.head()
sns.displot(dfc['not_distracted'])
Quickly Creating Summary Counts in Pandas¶
Let's next count the number of samples for each species. We can do this in a few
ways, but we'll use groupby combined with a count() method.
surveys_df = pd.read_csv("../../csv/surveys.csv")
# Count the number of samples by species
species_counts = surveys_df.groupby('species_id')['record_id'].count()
print(species_counts)
Or, we can also count just the rows that have the species "DO":
surveys_df.groupby('species_id')['record_id'].count()['DO']
Basic Math Functions¶
If we wanted to, we could perform math on an entire column of our data. For example let's multiply all weight values by 2. A more practical use of this might be to normalize the data according to a mean, area, or some other value calculated from our data.
# Multiply all weight values by 2 but does not change the original weight data
surveys_df['weight']*2
Quick & Easy Plotting Data Using Pandas¶
We can plot our summary stats using Pandas, too.
## To make sure figures appear inside Jupyter Notebook
%matplotlib inline
# Create a quick bar chart
species_counts.plot(kind='bar')
Animals per site plot¶
We can also look at how many animals were captured in each site.
total_count = surveys_df.groupby('site_id')['record_id'].nunique()
# Let's plot that too
total_count.plot(kind='bar')
Extra Plotting Challenge¶
Create a plot of average weight across all species per plot.
Create a plot of total males versus total females for the entire dataset.
Create a stacked bar plot, with weight on the Y axis, and the stacked variable being sex. The plot should show total weight by sex for each plot. Some tips are below to help you solve this challenge: For more on Pandas plots, visit this link.
Solution to Extra Plotting Challenge 1¶
## Solution Plotting Challenge 1
surveys_df.groupby('site_id').mean()["weight"].plot(kind='bar')
Solution to Extra Plotting Challenge 2¶
# Solution Plotting Challenge 2
## Create plot of total males versus total females for the entire dataset.
surveys_df.groupby('sex').count()["record_id"].plot(kind='bar')
Solution to Extra Plotting Challenge 3¶
First we group data by site and by sex, and then calculate a total for each site.
by_site_sex = surveys_df.groupby(['site_id','sex'])
site_sex_count = by_site_sex['weight'].sum()
This calculates the sums of weights for each sex within each plot as a table
site sex
site_id sex
1 F 38253
M 59979
2 F 50144
M 57250
3 F 27251
M 28253
4 F 39796
M 49377
<other sites removed for brevity>Below we'll use .unstack() on our grouped data to figure out the total weight that each sex contributed to each plot.
by_site_sex = surveys_df.groupby(['site_id','sex'])
site_sex_count = by_site_sex['weight'].sum()
site_sex_count.unstack()
Now, create a stacked bar plot with that data where the weights for each sex are stacked by plot.
Rather than display it as a table, we can plot the above data by stacking the values of each sex as follows:
by_site_sex = surveys_df.groupby(['site_id', 'sex'])
site_sex_count = by_site_sex['weight'].sum()
spc = site_sex_count.unstack()
s_plot = spc.plot(kind='bar', stacked=True, title="Total weight by site and sex")
s_plot.set_ylabel("Weight")
s_plot.set_xlabel("Site")
