Suppose I have a data frame
name = ['A', 'B', 'C']
score = [2,4,6]
I want to create a scatter plot with the following conditions, color the bubble as green if the score is greater than 3 and red otherwise. I'd also like to label the bubble with its respective name.
I'm only able to create a scatter plot with the bubble having the respective name.
j.doe
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asked May 11, 2019 at 7:21
You can use list comprehension to create a list of the colors for every score and use the c parameter of scatter[] to set the color inside the plot.
To lable the bubbles you can use annotate[] on the axis, see an example below.
import matplotlib.pyplot as plt
name = ['A', 'B', 'C']
score = [2,4,6]
# Set color for every score
color = ['green' if x>3 else 'red' for x in score]
# Create scatter plot
fig, ax = plt.subplots[]
ax.scatter[name, score, c=color]
# Set label for every score inside scatter plot
for i, n in enumerate[name]:
ax.annotate[n, [n,score[i]]]
plt.show[]
answered May 11, 2019 at 10:24
iljailja
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A conditioning plot or co-plot or subset plot is a scatter plot of two variables when conditioned on a third variable. The third variable is called the conditioning variable. This variable can have both values either continuous or categorical. In the continuous variable, we created subsets by dividing them into a smaller range of values. In categorical variables, the subsets are created based on different categories.
Let’s take three variables X, Y and Z. Z be the variable which we divided into the k groups. Here, there are many ways in which a group can be formed such as:
- By dividing the data into equal size of k groups.
- By dividing the data into different clusters on the basis of scatter plot.
- By dividing the range of data points into equal values.
- The categorical data have natural grouping on the basis of different categories of the dataframe.
Then, we plot n rows and m columns matrix where n*m >= k. Each set of [row, column] represents an individual scatter plot, in which each scatters plot consists of the following components.
- Vertical Axis: Variable Y
- Horizontal Axis: Variable X
where, points in the group corresponding to row i and column j are used.
The conditioning plot provides the answer to the following questions:
- Is there any relationship between the two variables?
- If there is a relationship then, does the nature of the relationship depend upon the third variable?
- Do different groups in the data behave similarly?
- Are there any outliers in the data?
Implementation
Python3
% matplotlib inline
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
titanic_dataset =pd.read_csv['train.csv']
titanic_dataset.head[]
sns.lmplot[x='Age', y ='Fare',hue='Survived', col ='Sex',data=titanic_dataset]
sns.lmplot[x='Age', y ='Fare',hue='Survived', col ='Pclass',data=titanic_dataset]
df1, df2 = titanic_dataset.loc[titanic_dataset['Age'] < 20 ] ,
titanic_dataset.loc[titanic_dataset['Age'] >= 20 ]
lm = sns.lmplot[x='Parch', y ='Fare',hue='Survived',data=df1]
ax1 =lm.axes
ax1=plt.gca[]
ax1.set_title['Age < 20']
lm_2 = sns.lmplot[x='Parch', y ='Fare',hue='Survived',data=df2]
ax2 =lm_2.axes
ax2=plt.gca[]
ax2.set_title['Age >= 20']
Conditional Plot on the basis of Sex
Conditional Plot on the basis of Passenger_Class
Conditional Plot on the basis of Age
References:
- NIST handbook