Shulou(Shulou.com)06/02 Report--

Python Seaborn library how to use, many novices are not very clear about this, in order to help you solve this problem, the following editor will explain in detail for you, people with this need can come to learn, I hope you can gain something.

What is Seaborn?

Seaborn is an advanced visualization effect library based on matplotlib, which is mainly aimed at the feature selection of variables in data mining and machine learning. Seaborn can use short code to draw visualization effects that describe more dimensional data. Even people who do not have any foundation can make analytical and beautiful graphics through minimalist code.

The official link is: Seaborn official link [1]

Seaborn provides the following functions:

Dataset-oriented API for checking relationships between multiple variables specifically supporting the use of classified variables to display observations or aggregate statistics to visualize univariate or bivariate distributions and options for comparison between data subsets automatic estimation and mapping of linear regression models of different kinds of dependent variables for constructing high-level abstractions of multi-graph grids Allows you to easily build complex visualization matplotlib graphic styles with several built-in themes for precise control and select tools that faithfully display palettes of patterns in the data

Advantages of seaborn

Advantages

Simplifies the representation of complex data sets; can easily build complex visualization and succinctly control matplotlib graphic styles and several built-in themes; seaborn can not replace matplotlib, but is a good supplement to matplotlib; it is easy for beginners to use with minimalist mode

Picture column display

1. Scatter plot matrix

Sns.pairplot (iris,hue= "species", palette= "Set2", diag_kind= "kde", height=2.5)

2. Violin picture

Sns.violinplot (x = "day", y = "total_bill", hue= "smoker", split=True, inner= "quart", palette= {"Yes": "y", "No": "b"}, data=tips)

4. Linear graph

# shared across the facetspalette = dict (zip (dots.coherence.unique (), sns.color_palette ("rocket_r", 6)) # Plot the lines on two facetssns.relplot (x = "time", y = "firing_rate", hue= "coherence", size= "choice", col= "align", size_order= ["T1", "T2"], palette=palette,height=5, aspect=.75, facet_kws=dict (sharex=False), kind= "line", legend= "full", data=dots)

5. FacetGrid of custom projection

Import numpy as npimport pandas as pdimport seaborn as snssns.set () # Generate an example radial datastr = np.linspace (0,10, num=100) df = pd.DataFrame ({'ritual: r,' slow': r, 'medium': 2 * r,' fast': 4 * r}) # Convert the dataframe to long-form or "tidy" formatdf = pd.melt (df, id_vars= ['r'], var_name='speed', value_name='theta') # Set up a grid of axes with a polar projectiong = sns.FacetGrid (df Col= "speed" hue= "speed", subplot_kws=dict (projection='polar'), height=4.5,sharex=False, sharey=False, despine=False) # Draw a scatterplot onto each axes in the gridg.map (sns.scatterplot, "theta", "r")

6. Tree diagram

Import pandas as pdimport seaborn as snssns.set () # Load the brain networks example datasetdf = sns.load_dataset ("brain_networks", header= [0,1,2], index_col=0) # Select a subset of the networksused_networks = [1,5,6,7,8,12,13 17] used_columns = (df.columns.get_level_values ("network") .astype (int) .isin (used_networks)) df = df.loc [:, used_columns] # Create a categorical palette to identify the networksnetwork_pal = sns.husl_palette (8, sound.45) network_lut = dict (map (str, used_networks) Network_pal) # Convert the palette to vectors that will be drawn on the side of the matrixnetworks = df.columns.get_level_values ("network") network_colors = pd.Series (networks, index=df.columns) .map (network_lut) # Draw the full plotsns.clustermap (df.corr (), center=0, cmap= "vlag", row_colors=network_colors, col_colors=network_colors,linewidths=.75, figsize= (13,13)) does it help you to read the above content? If you want to know more about the relevant knowledge or read more related articles, please follow the industry information channel, thank you for your support.

Welcome to subscribe "Shulou Technology Information " to get latest news, interesting things and hot topics in the IT industry, and controls the hottest and latest Internet news, technology news and IT industry trends.