Lets visualize our data with Heatmap which is present in Seaborn library. Heatmap is full of colors. Darker the color, higher is the value and vice versa. Values closer to 1 represent higher values and values closer to 0 represent lower values. We will use Flights dataset and analyze it through heatmap. We can pass various parameters to heatmap like annot, fmt, vmin, vmax, cbar, cmap, linewidths, center etc. Lets explore heatmap in detail:
Step 1: Import required libraries
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
#matplotlib inline
Step 2: Load Flights datasets
flights = sns.load_dataset('flights')
flights.head()
flights.tail()
Step 3: Explore data using Heat Map
Please note that I am not displaying the resulting maps in this post. Please explore it yourself in your Jupyter notebook.
Before exploring Flights dataset with Heatmap, lets first analyze some random numbers using Heatmap:
numbers = np.random.randn(12, 15)
numbers
sns.heatmap(numbers)
sns.heatmap(numbers, annot=True) #to show actual values in the heatmap
sns.heatmap(numbers, annot=True, vmin=0, vmax=2) #to change the key value of heatmap, by default key varies from 0 and 1.
sns.heatmap(flights, cbar=False) #to hide the color bar
Now, lets jump to our Flights dataset. Lets pivot this dataset so that we have "year" on x-axis and "month" on y-axis.
flights = flights.pivot('month', 'year', 'passengers')
flights
sns.heatmap(flights)
sns.heatmap(flights, annot=True)
sns.heatmap(flights, annot=True, fmt='d') #format the annotation to contain only digits
sns.heatmap(flights, annot=True, fmt='d', linewidths=0.9) #add linewidth to heatmap
sns.heatmap(flights, annot=True, fmt='d', linewidths=0.9, cmap='RdBu') #add color map to heatmap to change the color
sns.heatmap(flights, annot=True, fmt='d', linewidths=0.9, cmap='summer')
sns.heatmap(flights, annot=True, fmt='d', linewidths=0.9, cmap='winter_r')
sns.heatmap(flights, annot=True, fmt='d', linewidths=0.9, cmap='coolwarm')
sns.heatmap(flights, annot=True, fmt='d', center=flights.loc['June', 1954]) #center color theme to a particular cell
Step 1: Import required libraries
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
#matplotlib inline
Step 2: Load Flights datasets
flights = sns.load_dataset('flights')
flights.head()
flights.tail()
Step 3: Explore data using Heat Map
Please note that I am not displaying the resulting maps in this post. Please explore it yourself in your Jupyter notebook.
Before exploring Flights dataset with Heatmap, lets first analyze some random numbers using Heatmap:
numbers = np.random.randn(12, 15)
numbers
sns.heatmap(numbers)
sns.heatmap(numbers, annot=True) #to show actual values in the heatmap
sns.heatmap(numbers, annot=True, vmin=0, vmax=2) #to change the key value of heatmap, by default key varies from 0 and 1.
sns.heatmap(flights, cbar=False) #to hide the color bar
Now, lets jump to our Flights dataset. Lets pivot this dataset so that we have "year" on x-axis and "month" on y-axis.
flights = flights.pivot('month', 'year', 'passengers')
flights
sns.heatmap(flights)
sns.heatmap(flights, annot=True)
sns.heatmap(flights, annot=True, fmt='d') #format the annotation to contain only digits
sns.heatmap(flights, annot=True, fmt='d', linewidths=0.9) #add linewidth to heatmap
sns.heatmap(flights, annot=True, fmt='d', linewidths=0.9, cmap='RdBu') #add color map to heatmap to change the color
sns.heatmap(flights, annot=True, fmt='d', linewidths=0.9, cmap='summer')
sns.heatmap(flights, annot=True, fmt='d', linewidths=0.9, cmap='winter_r')
sns.heatmap(flights, annot=True, fmt='d', linewidths=0.9, cmap='coolwarm')
sns.heatmap(flights, annot=True, fmt='d', center=flights.loc['June', 1954]) #center color theme to a particular cell
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