seaborn.heatmap(data, *, vmin=None, vmax=None, cmap=None, center=None, robust=False, annot=None, fmt='.2g', annot_kws=None, linewidths=0, linecolor='white', cbar=True, cbar_kws=None, cbar_ax=None, square=False, xticklabels='auto', yticklabels='auto', mask=None, ax=None, **kwargs)#

Practice now: Test your Python skills with interactive challenges · The heatmap plot below is based on random values generated by numpy. Many parameters are possible, this just shows the most basic plot. ... import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt np.random.seed(0) sns.set() uniform_data = np.random.rand(10, 12) ax = sns.heatmap(uniform_data, vmin=0, vmax=1) plt.show()

July 23, 2025 - DSA Python · Data Science · NumPy · Pandas · Practice · Django · Flask · Last Updated : 23 Jul, 2025 · A heatmap is a graphical representation of data where individual values are represented by color intensity.

December 13, 2024 - Learn to create 15 types of data plots in Python using matplotlib and seaborn, from common bar charts to advanced heatmaps and unconventional treemaps.

Plot a heatmap for a numpy array: >>> import numpy as np; np.random.seed(0) >>> import seaborn as sns; sns.set_theme() >>> uniform_data = np.random.rand(10, 12) >>> ax = sns.heatmap(uniform_data) Change the limits of the colormap: >>> ax = sns.heatmap(uniform_data, vmin=0, vmax=1) Plot a heatmap for data centered on 0 with a diverging colormap: >>> normal_data = np.random.randn(10, 12) >>> ax = sns.heatmap(normal_data, center=0) Plot a dataframe with meaningful row and column labels: >>> flights = sns.load_dataset("flights") >>> flights = flights.pivot("month", "year", "passengers") >>> ax = s

If you're in a rush and want to make a heatmap with Python as quick as possible, have a look to this code snippet that uses the heatmap() function of Seaborn.🔥

September 27, 2023 - To transform this into the wide-form DataFrame needed by Seaborn we can use the Pandas pivot() function. For this example, it will be easiest to take one user at a time and plot a heatmap for each of them separately.

February 9, 2026 - Top 10 growing data visualization libraries in Python in 2025 · The core function is sns.heatmap(). It accepts a 2D dataset -- typically a pandas DataFrame or a NumPy array -- and renders it as a colored grid. If your data needs reshaping before plotting, operations like pivot tables are often ...

March 10, 2024 - Data visualization encompasses ... library built upon Matplotlib, offers an advanced platform for creating visually pleasing and informative statistical graphics, including heatmaps....

October 11, 2022 - import numpy as np import seaborn as sns # Data simulation np.random.seed(1) data = np.random.rand(10, 10) sns.heatmap(data, linewidths = 0.75, linecolor = "white") # Default color

seaborn components used: set_theme(), load_dataset(), heatmap() import matplotlib.pyplot as plt import seaborn as sns sns.set_theme() # Load the example flights dataset and convert to long-form flights_long = sns.load_dataset("flights") flights = ( flights_long .pivot(index="month", columns="year", values="passengers") ) # Draw a heatmap with the numeric values in each cell f, ax = plt.subplots(figsize=(9, 6)) sns.heatmap(flights, annot=True, fmt="d", linewidths=.5, ax=ax)

September 8, 2019 - Python seaborn heatmap is a graphical representation of 2D data. Each data value represents in a matrix and it has a special color show using sns.heatmap()

May 18, 2022 - The primary goal is to help you effectively use Seaborn to visualize single-day percentage price changes of stocks and explore correlations among these changes. We cover the basics, such as understanding what a heatmap is, and then delve into specific use cases within the financial domain. The tutorial includes clear Python code for each step, making it accessible for both beginners and those looking to enhance their data visualization skills.

%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() Default plot · sns.heatmap(df) <matplotlib.axes._subplots.AxesSubplot at 0x10c53b7b8> cmap adjusts the colormap used.

For continuous data, though, the appropriate thing is to choose from two other types, either a sequential colormap, or a diverging colormap. The default colormap for both seaborn.heatmap and seaborn.clustermap is 'rocket,' a sequential colormap included with seaborn.

seaborn 0.10.1 · Gallery · Tutorial · API · Site · Introduction · Release notes · Installing · Example gallery · Tutorial · API reference · Page · Annotated heatmaps · Python source code: [download source: heatmap_annotation.py] import matplotlib.pyplot as plt import seaborn as sns sns.set() # Load the example flights dataset and convert to long-form flights_long = sns.load_dataset("flights") flights = flights_long.pivot("month", "year", "passengers") # Draw a heatmap with the numeric values in each cell f, ax = plt.subplots(figsize=(9, 6)) sns.heatmap(flights, annot=True, fmt="d", linewidths=.5, ax=ax)

August 30, 2022 - plt.figure(figsize=(9,5) sns.heatmap(df.corr(),annot=True)

January 18, 2021 - #import seaborn import seaborn ... ... The x-axis displays the year, the y-axis displays the month, and the color of the squares within the heatmap represent the number of flights in those particular year-month combinations...