Python Data Visualization

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Python Tutorial

Plotly

Python Plotly

Seaborn

Kaggle Data Visualization
Extra Day 19

  • Trends - A trend is defined as a pattern of change.
    • sns.lineplot - Line charts are best to show trends over a period of time, and multiple lines can be used to show trends in more than one group.
  • Relationship - There are many different chart types that you can use to understand relationships between variables in your data.
    • sns.barplot - Bar charts are useful for comparing quantities corresponding to different groups.
    • sns.heatmap - Heatmaps can be used to find color-coded patterns in tables of numbers.
    • sns.scatterplot - Scatter plots show the relationship between two continuous variables; if color-coded, we can also show the relationship with a third categorical variable.
    • sns.regplot - Including a regression line in the scatter plot makes it easier to see any linear relationship between two variables.
    • sns.lmplot - This command is useful for drawing multiple regression lines, if the scatter plot contains multiple, color-coded groups.
    • sns.swarmplot - Categorical scatter plots show the relationship between a continuous variable and a categorical variable.
  • Distribution - We visualize distributions to show the possible values that we can expect to see in a variable, along with how likely they are.
    • sns.distplot - Histograms show the distribution of a single numerical variable.
    • sns.kdeplot - KDE plots (or 2D KDE plots) show an estimated, smooth distribution of a single numerical variable (or two numerical variables).
    • sns.jointplot - This command is useful for simultaneously displaying a 2D KDE plot with the corresponding KDE plots for each individual variable.

Dependencies

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import pandas as pd
pd.plotting.register_matplotlib_converters()
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns

Line Charts

Line chart of whole df

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# Set the width and height of the figure
plt.figure(figsize=(14,6))

# Add title
plt.title("title")

# Line chart showing all columns
sns.lineplot(data=df)

Line chart of parts of df

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# Set the width and height of the figure
plt.figure(figsize=(14,6))

# Add title
plt.title("title")

# Line chart showing df column 1
sns.lineplot(data=df['col1'], label="feature 1")

# Line chart showing df column 2
sns.lineplot(data=df['col2'], label="feature 2")

# Add label for horizontal axis
plt.xlabel("xlabel")

# Add label for vertical axis
plt.ylabel("ylabel")

Bar Charts and Heatmaps

Bar chart

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# Set the width and height of the figure
plt.figure(figsize=(10,6))

# Add title
plt.title("title")

# Bar chart showing df column1 by df.index
sns.barplot(x=df.index, y=df['col1'])

# Add label for vertical axis
plt.ylabel("ylabel")

Heatmap

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# Set the width and height of the figure
plt.figure(figsize=(14,7))

# Add title
plt.title("title")

# Heatmap
sns.heatmap(data=df, annot=True)

# Add label for horizontal axis
plt.xlabel("xlabel")

Scatter Plots, Regression, and Categorical Scatter Plots

Scatter Plot

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sns.scatterplot(x=df['col1'], y=df['col2'])

Regression Line + Scatter Plot

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sns.regplot(x=df['col1'], y=df['col2'])

Group by color

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sns.scatterplot(x=df['col1'], y=df['col2'], hue=df['col3'])

Regression Line per Group (hue)

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sns.lmplot(x="col1", y="col2", hue="col3", data=df)

Categorical Scatter plot: swarmplot

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# x categorical data
# y numerical data
sns.swarmplot(x=df['col1'], y=df['col2'])

Histograms and Density Plots

Histograms

  • x=: chooses the column we’d like to plot.
  • kde=False: Whether to plot a gaussian kernel density estimate..
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sns.histplot(x=df['col'], kde=False)

Density Plots (KDE)

  • fill: If True, fill in the area under univariate density curves or between bivariate contours. If None, the default depends on multiple.
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sns.kdeplot(x='col1', data=df, fill=True)

2D KDE Plots

Draw a plot of two variables with bivariate and univariate graphs.

  • kind: kind : { “scatter” | “kde” | “hist” | “hex” | “reg” | “resid” }
    • Kind of plot to draw. See the examples for references to the underlying functions.
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sns.jointplot(x='col1', y='col2', data=df, kind="kde")

Color-coded plots (multiple histograms)

  • label= is used to set the values in the legend.
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sns.distplot(a='col', data=df1, label='label1', kde=False)
sns.distplot(a='col', data=df2, label='label2', kde=False)
sns.distplot(a='col', data=df3, label='label3', kde=False)

# Add title
plt.title("title")

# Force legend to appear
plt.legend()

Choosing Plot Types and Custom Styles

Seaborn has five different themes: (1)”darkgrid“, (2)”whitegrid“, (3)”dark“, (4)”white“, and (5)”ticks“.

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# Change the style of the figure to the "dark" theme
sns.set_style("dark")

# Line chart 
plt.figure(figsize=(12,6))
sns.lineplot(data=spotify_data)

Multi-plot Grids

Building structured multi-plot grids