First things first! Download these palmer penguins data into your working directory. Note, you may need to use pip3. Into a terminal, type in:
pip -q install palmerpenguins
Make sure you have these installed too.
pip install numpy
pip install pandas
pip install seaborn
pip install palmerpenguins
Start a new notebook. Save the file as “yourname_week3.ipynb”. As before, copy the code into your notebook as chunks.
Instead of using your notebook straightaway, run an interactive python session in a terminal. In your python session:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
Note: This type of data exploration is a data science approach to understanding and interrogating a dataset to either answer a question, research topic or just around basic curiosity. You will be repeating things like these for most types of analysis.
A data type we didn’t explicitly speak about are data frames.
.DataFrame() function.
data = {
"pandas": ["Alex", "Bob", "Carl"],
"age": [5, 4, 5]
}
df = pd.DataFrame(data)
print(df)
from palmerpenguins import load_penguins
df = load_penguins()
print(type(df))
print(df)
print(df.describe()) # Summary statistics of numerical data
print(df.dtypes) # What data types are in the data frame
i=1 j=0
print(df.loc[i]) # Returns the ith row print(df.iloc[i,j]) # Returns the ith and jth element
print(df[[‘bill_length_mm’,’island’]]) # Returns the two columns specified print(df.query(“year > 2007”)) # Filters the data based on the year column
# Q1: Test yourself!
### In your jupyter notebook, create new chunk for this question. In this chunk, write some code to access and print out the properties of the first two _columns_ from the `df` variable.
## Plotting
Now that we have our data loaded and a basic idea of how to manipulate it and what it contains, let's get started on visualising it. Before we do that, we will go over some of the basic plotting functions.
The matplotlib package is the standard go-to for this. In addition, the seaborn package is an extension of this for statistical data visualisation. We should have imported both of these earlier, but just in case:
import matplotlib.pyplot as plt import seaborn as sns
The standard plotting functions in matplot include:
- plot: x-y plots
- scatter: scatter plot
- hist: histograms, to plot distributions
- boxplot: box and whisker plots
- bar: bar plots
- others: pie, lmplot, etc.
More here: https://www.w3schools.com/python/matplotlib_intro.asp
- Each plot has additional built-in features, such as adding axis titles and labels, and ways to make your plots look "pretty". More on this later.
### Basic x-y plot:
x = [1,2,3]
y = [4,5,6]
plt.plot(x, y)
plt.xlabel(‘x - axis’) plt.ylabel(‘y - axis’)
plt.title(‘X-Y’)
plt.show()
You can also add points to the plot by specifiying the plot type. And if we don't give an x and y, the x is the "index".
a = [1, 2, 3, 4, 5] b = [0, 0.6, 0.2, 15, 10, 8, 16, 21] plt.plot(a)
plt.plot(b, “o”)
ax = plt.gca() ax.legend([‘Line’, ‘Dots’])
plt.show()
There are many more plotting features you can play with, including colors, figure size (figure()), text (annotate()) and multiple plots (subplot()).
fig = plt.figure(figsize =(10, 5)) sub1 = plt.subplot(2, 1, 1) # two plots, or 2 rows, one column, position 1 sub2 = plt.subplot(2, 1, 2) # two plots, or 2 rows, one column, position 2 sub1.plot(a, ‘sb’) # squares, blue sub1.annotate(“Squares”, (1,1)) sub2.plot(b, ‘or’) # circles, red sub2.annotate(“Circles”, (1,1)) plt.show()
Read more here:
https://matplotlib.org/stable/gallery/lines_bars_and_markers/simple_plot.html
https://www.geeksforgeeks.org/simple-plot-in-python-using-matplotlib/
# Q2: Test yourself!
### In your jupyter notebook, create new chunk for this question. In this chunk, write some code to create two variables `m` and `n` that are correlated to each other. You can do this by first creating values for `m`. If you remember from last week, we generated a single value with the random package. Here, we can use the numpy package to generate multiple numbers. e.g., m = np.random.normal(0,1,100), where 0 is the mean, 1 is the standard deviation, and 100 is the number of values to generate. To get correlated values `n`, we generate some "noise" (e.g, `noise = np.random.normal(0,0.1,100)`) and add that signal to `m` (assigning it to `n`). Then plot `m` versus `n` variables (don't forget the "o" variable!). Note how we decreased the standard deviation when generating the noise signal. See what happens when you play around with that value by increasing and decreasing it and plotting those two graphs again.
### Penguin plots
We will use the seaborn module (which we named sns) to plot the palmer penguin data. The plotting functions in seaborn can be organised around three categories.
- Relational plots: relplot, scatterplot, lineplot, ...
- Distributions: histplot, displot, kdeplot, rugplot, ecdfplot, ...
- Categorical: stripplot, swarmplot, boxplot, violinplot, pointplot, barplot, ...
Addtionally, we can combine these with functions like:
- jointplot, subplots, etc
#### Exploratory plots
First, let's take a look at the distribution of bill lengths. Here we use the histplot function, and add a density line.
sns.histplot(df[‘bill_length_mm’],kde=True,bins=20)
Another plot type are "joint" or scatter plots with histograms. Here we can plot the bill length versus the bill depth, along with their distributions as histograms.
sns.jointplot(data=df, x=”bill_length_mm”, y=”bill_depth_mm”)
We can also plot all the different data, pairwise. Note, some of these plots look weird.
sns.pairplot(df)
The seaborn package also lets us draw boxplots. Here we can split them by island and species.
g = sns.boxplot(x = ‘island’, y =’body_mass_g’, hue = ‘species’, data = df, palette=[‘#FF8C00’,’#159090’,’#A034F0’], linewidth=0.3) g.set_xlabel(‘Island’) g.set_ylabel(‘Body Mass’) plt.show()
Plot the flipper length and add regression models per species:
g = sns.lmplot(x=”flipper_length_mm”, y=”body_mass_g”, hue=”species”, height=7, data=df, palette=[‘#FF8C00’,’#159090’,’#A034F0’]) g.set_xlabels(‘Flipper Length’) g.set_ylabels(‘Body Mass’) plt.show()
# Q3: Test yourself!
### In your jupyter notebook, create new chunk for this question. In this chunk, we will try to repeat similar plots with the `iris` dataset. Using the `describe` function, look at the variables in the iris dataset, and make a distribution plot for one of them.
#### Multiple figures
- Use subplots() to create multiple figures.
- Body mass, four ways:
import matplotlib.pyplot as pltt fig ,ax = pltt.subplots(figsize=(15,12), ncols=2,nrows=2) sns.swarmplot(data=df,x=’species’,y=’body_mass_g’,ax=ax[0,0],hue=’species’) sns.violinplot(data=df,x=’species’,y=’body_mass_g’,ax=ax[0,1]) sns.boxplot(data=df,x=’species’,y=’body_mass_g’,ax=ax[1,0]) sns.barplot(data=df,x=’species’,y=’body_mass_g’,ax=ax[1,1]) pltt.show()
See the documentation at https://pandas.pydata.org/docs/user_guide/io.html
## Other
### Show the help message for this function.
df.to_csv?
### Save to a file, load from file
Let's save the penguin data to a file. Then load it into another variable.
df.to_csv(“my_penguins.csv”) df_penguins = pd.read_csv(“my_penguins.csv”) df_penguins.head() ```
iris dataset. Plot a multiple plot figure, with any variable in the iris dataset, across the four different plot types.[Solutions next week]
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