This is a code flashcard, the point is to be able to be able to write simple code examples and understand them quickly. Meant to be solved in a simple IDE, without code completion.
Basic example:
Plot some connected points:
x = np.array([1,2,3,4])
y = x**2
What if you want to plot a function?
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Basic example:
plot some connected points
import matplotlib.pyplot as plt
import numpy as np
x = np.array([1,2,3,4])
y = x**2
plt.plot(x, y, marker='o', linestyle='-', color='g')
plt.title("plot title")
plt.xlabel("x")
plt.ylabel('f(x)')
plt.show()
Plot a function
Exactly as before, just with a really high number of points. {python} np.linspace(start, stop, number). Don't overthink it.
Subplots
Create two vertically stacked subplots with a title.
x_0 = np.linspace(0, 2 * np.pi, 400)
y_0 = np.sin(x_0 ** 2)
x_1 = np.linspace(0, 2 * np.pi, 400)
y_1 = - np.sin(x_1 ** 2)
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Subplots
import matplotlib.pyplot as plt
import numpy as np
x_0 = np.linspace(0, 2 * np.pi, 400)
y_0 = np.sin(x ** 2)
x_1 = np.linspace(0, 2 * np.pi, 400)
y_1 = - np.sin(x ** 2)
fig, axs = plt.subplots(2, 1) # 2 rows, 1 column
# there is only one fig, but multiple axes.
# axs has shape (2,)
fig.suptitle('Vertically stacked subplots')
axs[0].plot(x_0, y_0)
axs[1].plot(x_1, y_1)
Histograms
Create a simple histogram with 10 bins.
x = np.random.normal(170, 10, 250)
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Histograms
Histograms are used to visualise numerical data distributions. If you want to visualize discrete data distributions, see Bar Graphs
import matplotlib.pyplot as plt
import numpy as np
x = np.random.normal(170, 10, 250)
# the bins are the amount of "categories" in the x axis
plt.hist(x, bins=10)
plt.xlabel("bin means")
plt.ylabel("amount of elements in bin")
plt.show()
Box plots:
Create box plots in matplotlib.
a = np.random.uniform(low=0, high=50, size=1000)
b = np.random.uniform(low=10, high=200, size=1000)
c = np.random.uniform(low=20, high=30, size=1000)
When/Why do you use a box plot?
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Box Plots
Used to visualise the distributions of numerical data. Has the advantage that it is an extremely fast overview.
a = np.random.uniform(low=0, high=50, size=1000)
b = np.random.uniform(low=10, high=200, size=1000)
c = np.random.uniform(low=20, high=30, size=1000)
d = {"firstCol": a, "secondCol": b, "thirdCol": c}
df = pd.DataFrame(data=d)
boxplot = df.boxplot()
These comparisons only make sense, if the data is actually comparable. The Y column needs to mean the same for every column.
Bar graphs
Create a Bar graph in matplotlib
import pandas as pd
import random
# Let's create our own pandas dataframe:
colourList = []
for _ in range(100):
r = random.randint(0,100)
if r <= 50:
colourList.append("Blue")
continue
if r <=80:
colourList.append("Purple")
continue
# if value above 80
colourList.append("Red")
When do you use Bar graphs?
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Bar graphs
Used to visualise discrete data distribution.
import pandas as pd
import random
# Let's create our own pandas dataframe:
colourList = []
for _ in range(100):
r = random.randint(0,100)
if r <= 50:
colourList.append("Blue")
continue
if r <=80:
colourList.append("Purple")
continue
# if value above 80
colourList.append("Red")
d = {"favourite_colour": colourList}
df = pd.DataFrame(data=d)
# counts in a pandas dataframe itself that can be plotted.
counts = df.value_counts()
counts.plot(kind='bar')
plt.show()
Scatter Plots
Create a simple scatter plot in matplotlib.
Why would you use scatter plots?
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Scatter Plots
Used to find clusters or patterns in the data that are visible if you look at the entirety of the dataset
first_c_x = np.random.uniform(low=0, high=20, size=100)
first_c_y = np.random.uniform(low=10, high=40, size=100)
second_c_x = np.random.uniform(low=45, high=75, size=100)
second_c_y = np.random.uniform(low=60, high=90, size=100)
plt.scatter(first_c_x, first_c_y, s=3, c="r")
plt.scatter(second_c_x, second_c_y, s=4, c="blue")
plt.show()
Heatmaps
Create a simple heatmap from a Dataframe/Dictionary
import pandas as pd
# Define your data as a dictionary
X = {
"Column1": [10, 40, 30],
"Column2": [10, 60, 30],
"Column3": [10, 20, 30],
}
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Heatmaps
For Heatmaps we use the seaborn library. It is a library built on top of matplotlib. They are particularly useful to quickly visualise data and notice correlations.
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# X being my Dataframe
correlation_matrix = X.corr()
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.show()
Violin Plots
Create a simple violin plot in matplotlib
X = {
"Column1": [10, 20, 30],
"Column2": [10, 40, 30],
"Column3": [10, 50, 30],
}
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Violin Plot
If the data is in categories, and is comparable between categories (same unit for example), to compare them you need to do as many plots as there are categories. It is an advanced form of the Box plots but might show more variation in the data
import pandas as pd
df = pd.Dataframe(...)
plt.violinplot(df)
plt.xticks(ticks=range(1, len(df.columns) + 1), labels=df.columns)
plt.title("violin plot of supermarket purchases")
plt.xlabel("spending categories")
plt.ylabel("Money spent in €")
plt.show()
import math
theta = 1 # example value
for column in X:
X[column] = X[column].apply(lambda x: math.log(float(x) + theta))
Grid Layout
Create a simple image grid (3 rows, 3 columns) with the same image in matplotlib. Images, not matrices.
from PIL import Image
img = Image.open("cat.jpg")
# tip: do it as a function.
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Grid layout
Example with fig.add_subplot
def create_image_grid(images, show_axis=True):
amt_images = len(images)
if amt_images > 9:
raise ValueError("Can only visualize up to 9 images at once.")
# we want a max of 3 columns.
# it is important that both of these variables are integers!
amt_cols = min(3, amt_images)
amt_rows = int(np.ceil(amt_images / amt_cols))
fig = plt.figure()
for i, image in enumerate(images):
# Iterating over the grid returns the Axes.
ax = fig.add_subplot(amt_rows, amt_cols, i + 1)
ax.imshow(image)
if not show_axis:
ax.axis('off')
# adjust spacing between subplots.
plt.tight_layout()
plt.show()
example with plt.subplots:
def create_image_grid(images, global_title=None):
if amt_images > 9:
raise ValueError("Can only visualize up to 9 images at once.")
amt_images = len(images)
amt_cols = min(3, amt_images)
amt_rows = int(np.ceil(amt_images / amt_cols))
fig, axs = plt.subplots(amt_rows, amt_cols)
if global_title:
fig.suptitle(global_title)
for i, img in enumerate(images):
row, col = divmod(i, amt_cols)
axs[row][col].imshow(img)
# Globally turn off all axes
for ax in axs.flat:
ax.axis("off")
plt.show()
Small things in matplotlib
Create a horizontal line in matplotlib. Assume that you already have the variable {python}plt. Make sure it shows up in a legend.
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Create a vertical line in a plot. Assume that you already have the variable {python}plt.
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Set the limits of axis in a plot. Assume that you already have the variable {python}plt.
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Add a legend to a plot. Assume that already have the variable {python}plt.
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Please remove the axes from a plot. Assume you already have the variable {python}ax
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{python}ax.axis("off")
Divers
Please save a figure to folder: {python}"output/plots"
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- create the folder if it doesn't exist
import os
filepath = "output/plots"
os.makedirs(filepath, exist_ok=True)
- Save the figure
{python}plt.savefig(filepath)