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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 seaborn.heatmap
Heat maps display numeric tabular data where the cells are colored depending upon the contained value. Heat maps are great for making trends in this kind of data more readily apparent, particularly when the data is ordered and there is clustering.
dataset
Step1:  Default plot
Step2:  cmap adjusts the colormap used. I like diverging colormaps for heatmaps because they provide good contrast.
Step3:  center can be used to indicate at which numeric value to use the center of the colormap. Above we see most of the map using blues, so by setting the value of center equal to the midpoint of the data then we can create a map where there are more equal amounts of red and blue shades.
Step4:  Adjust the lower and upper contrast bounds with vmin and vmax. Everything below vmin will be the same color. Likewise for above vmax.
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Python Code:
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
plt.rcParams['figure.figsize'] = (20.0, 10.0)
plt.rcParams['font.family'] = "serif"
df = pd.pivot_table(data=sns.load_dataset("flights"),
                    index='month',
                    values='passengers',
                    columns='year')
df.head()
Explanation: seaborn.heatmap
Heat maps display numeric tabular data where the cells are colored depending upon the contained value. Heat maps are great for making trends in this kind of data more readily apparent, particularly when the data is ordered and there is clustering.
dataset: Seaborn - flights
End of explanation
sns.heatmap(df)
Explanation: Default plot
End of explanation
sns.heatmap(df, cmap='coolwarm')
Explanation: cmap adjusts the colormap used. I like diverging colormaps for heatmaps because they provide good contrast.
End of explanation
midpoint = (df.values.max() - df.values.min()) / 2
sns.heatmap(df, cmap='coolwarm', center=midpoint)
Explanation: center can be used to indicate at which numeric value to use the center of the colormap. Above we see most of the map using blues, so by setting the value of center equal to the midpoint of the data then we can create a map where there are more equal amounts of red and blue shades.
End of explanation
midpoint = (df.values.max() - df.values.min()) / 2
sns.heatmap(df, cmap='coolwarm', center=midpoint, vmin=150, vmax=400)
midpoint = (df.values.max() - df.values.min()) / 2
sns.heatmap(df, cmap='coolwarm', center=midpoint, vmin=-100, vmax=800)
midpoint = (df.values.max() - df.values.min()) / 2
p = sns.heatmap(df, cmap='coolwarm', center=midpoint)
p.get_figure().savefig('../../figures/heatmap.png')
 flights_long.pivot(index="month", columns="year", values='passengers')
Explanation: Adjust the lower and upper contrast bounds with vmin and vmax. Everything below vmin will be the same color. Likewise for above vmax.
End of explanation
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