2/17/22, 11:40 AM
Pandas Practice Code - Set 12 - Jupyter Notebook
localhost:8888/notebooks/Pandas Practice Code - Set 12.ipynb
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age 0.42 0.67 0.75 0.83 0.92 1.0 2.0 3.0 4.0 5.0 ... 63.0 64.0 65.0 66.0 70.0 70
class
First 0 0 0 0 1 0 1 0 1 0 ... 1 2 2 0 1
Second 0 1 0 2 0 2 2 3 2 1 ... 0 0 0 1 1
Third 1 0 2 0 0 5 7 3 7 3 ... 1 0 1 0 0
All 1 1 2 2 1 7 10 6 10 4 ... 2 2 3 1 2
4 rows × 89 columns
# The crosstab() function is used to plot cross-tabulation
# between two columns. Let’s import the Titanic dataset from
# the Seaborn library and plot a cross tab matrix between
# passenger class and age columns for the Titanic dataset.
# Look at the following two scripts on how to do that:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
# sets the default style for plotting
sns.set_style("darkgrid")
titanic_data = sns.load_dataset('titanic')
pd.crosstab(titanic_data["class"], titanic_data["age"], margins=True)
# Discretization and Binning
# Discretization or binning refers to creating categories or bins
# using numeric data. For instance, based on age, you may want
# to assign categories such as toddler, young, adult, and senior
# to the passengers in the Titanic dataset. You can do this using
# binning.
# Let’s see an example. The following script imports the Titanic
# dataset.
import matplotlib.pyplot as plt
import seaborn as sns
titanic_data = sns.load_dataset('titanic')
2/17/22, 11:40 AM
Pandas Practice Code - Set 12 - Jupyter Notebook
localhost:8888/notebooks/Pandas Practice Code - Set 12.ipynb
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In [5]:
Out[5]:
adult 513
young 135
toddler 44
senior 22
Name: age_group, dtype: int64
# You can use the cut method from the Pandas dataframe to
# perform binning. First, the column name is passed to the “x”
# attribute. Next, a list containing ranges for bin values is passed
# to the “bins” attribute, while the bin or category names are
# passed to the “labels” parameter.
# The following script assigns a category toddler to passengers
# between the ages 0–5, young to passengers aged 5–20, adult
# to passengers aged 20–60, and senior to passengers aged 60–
# 100.
titanic_data['age_group']=pd.cut(x = titanic_data['age'],
bins = [0,5,20,60,100],
labels = ["toddler", "young", "adult","senior"])
titanic_data['age_group'].value_counts()
2/17/22, 11:40 AM
Pandas Practice Code - Set 12 - Jupyter Notebook
localhost:8888/notebooks/Pandas Practice Code - Set 12.ipynb
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<class 'numpy.ndarray'>
# Visualization with NumPy and Matplotlib
# You are now going to learn how to create NumPy Ndarrays with Ndarray creation
# routines and then use Matplotlib to visualize them.
# The first routine is arange(). It creates evenly spaced values with the given interval
# A stop value argument is compulsory. The start value and interval parameters have the
# default arguments 0 and 1, respectively. Here’s an example:
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(6)
print(x)
print(type(x))
# Let’s go ahead and plot these numbers. For plotting in 2D, we need x-y pairs. Let’s
# keep it simple and say y = f(x) = x by running the following statement:
y=x+1
# Now, let’s use the function plot() to visualize this. It needs the values of x and y a
# the plotting options.
plt.plot(x, y, 'o--')
plt.show()
# The function show() displays the plot.
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Pandas Practice Code - Set 12 - Jupyter Notebook
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[2 4]
# Here’s an example of the function call for the function arange() with the start and
# stop arguments:
x = np.arange(2, 6)
print(x)
y = x + 1
# We can even add an argument for the interval as follows:
a= np.arange(2, 6, 2)
print(a)
b = a + 1
# We can draw multiple graphs as follows:
plt.plot(x, y, 'o--')
plt.plot(a, -b, 'o-')
plt.show()
# The output will have one line and another dashed line.
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Pandas Practice Code - Set 12 - Jupyter Notebook
localhost:8888/notebooks/Pandas Practice Code - Set 12.ipynb
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# You can even add a title to the graph as follows:
plt.plot(x, y, 'o--')
plt.plot(x, -y, 'o-')
plt.title('y=x and y=-x')
plt.show()
# The output will have a title as shown.
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Pandas Practice Code - Set 12 - Jupyter Notebook
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[ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15.]
# The function linspace(start, stop, number) returns an array of evenly spaced
# numbers over a specified interval. You must pass it the starting value, the end value,
# the number of values as follows:
N = 16
x = np.linspace(0, 15, N)
print(x)
# The previous code creates 11 numbers (0 to 10, both inclusive) as follows:
# [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15.]
# Let’s visualize this as follows:
y = x
plt.plot(x, y, 'o--')
plt.axis('off')
plt.show()
# The output will be:
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