It has been quite sometime since I used seaborn library and was finding it difficult to remember some basic functions from the package. Took some time out and did a deliberate practice session for a few hours

What did I relearn ?

  • seaborn works with matplotlib and updates the defaults to better defaults
  • seaborn uses a lot of default matplotlib functionality
  • seaborn works with pandas data frames. You can pass dataframes and let seaborn use the relevant variable
  • kdeplot= to plot density estimates
  • distplot has been deprecated
  • There are three distribution plot options available
    • histplot
    • ecdfplot
    • distplot
  • There are two types of plots - figure level plots and axes level plots
  • Figure level plots are relplot, displot and catplot
  • ecdfplot
    • central tendencies and summary stats cannot be seen
    • No binning
    • returns a matplotlib object and you can then manipulate the same
  • boxplot
    • a plot that shows the median, percentiles, the outliers
    • find the median, concentrate on the lower half and draw a median, concentrate on the upper half and draw a line. The median and the percentile points represent the box.
    • Find the IQR and stretch the whiskers by 1.5 times and show the whiskers
    • The points that lie outside of whiskers are the outliers
    • Strictly the whiskers are based on the actual values in the data
    • There are many more options for a user to customize the boxplot than what is mentioned in the seaborn=
  • violinplot
    • It is a categorical distribution plot
    • There are several similarities with box plot. You can see the median and percentiles
    • There are no outliers in the violin plot
    • violinplot is symmetric and hence there is an additional info
    • You take a KDE plot and create a symmetric version of kde plot and show the plot as violin
  • swarmplot
    • The name comes from the fact that the plots look like a swarm of bees
    • There is a dot for every single data point
    • this plot doesn’t scale well if you have lots of data
    • It gets you a sense of distribution of data
    • inherits from matplotlib’s scatter plot
  • stripplot
    • categorical scatterplot
    • Similar to swarmplot
    • swarmplot are arranged as separate and hence the width of the swarmplot can be used to infer the distribution of data
    • stripplot can be used in cases when there is large data where swarmplot might not make sense
    • use jitter to separate the points in the plot that take identical values
  • scatterplot
    • One of the most popular plots
    • There are a ton of options to add semantic elements to a simple x y plot.
    • One can overlay additional dimensionality on an x y plot using hue, size, style
  • lineplot
    • used to draw a line between two variables connecting means and bootstrapped confidence intervals
    • One can also disable the plotting of confidence intervals
    • One can also choose various estimators to plot instead of the standard average value
  • regplot
    • Used to plot a regression line and relevant confidence intervals for a set of data points
  • heatmap
    • Very useful for showing the variation across a matrix of values
    • parameters to control the label on both axis, annotation, font size of the annotation, the color palette that can be used and whole host of options
  • FacetGrid
    • One can use the variables in the dataframe and draw conditional plots. Based on the specified rows and columns in the argument, separate plots are drawn for each combination of row and column
  • displot
    • stands for distribution plot
    • distplot is deprecated
    • one distribution plot to rule them all
    • histplot, kdeplot, ecdfplot and rugplot comes along
    • It is a figure level visual
    • bivariate relationship can be plotted and appears similar to heatmap
    • FacetGrid can be leveraged
  • barplot
    • a categorical vs numerical variable plot.
    • For the data grouped by the categorical variable, the plot shows the mean of the numerical variable
    • There are also confidence intervals that are displayed. These CIs are computed via bootstrapping
  • countplot
    • count up the number of observations per category
    • histogram for categorical data
    • Overlay the count with another categorical variable with hue
    • different from barplot as the barplot is used to plot statistical summary of data grouped by a specific variable
    • more than 170 palettes to pick from
    • pass arguments to matplotlib
  • pairplot
    • This plot is applicable to all the numerical variables in a dataframe. Univariate and Bivariate relationships are all drawn at once on the plot.
    • The plots on the diagonal are either histogram or kde plot
    • The plots on the off-diagonal are either reg plot or scatter plot or kde plot
    • One can ofcourse use a categorical variable to visualize subplots for each category using color
    • The more I think about these plot, the more I think these plots are usually finished plots that can be used to communicate all relationships at once. One would obviously use univariate plots to understand each variable and probably a reg or scatter or kde plot to understand the way things move. It is unlikely that one would want to go for a pairplot all at once
  • jointplot
    • This plot is similar to pairplot in the sense that it plots univariate and join plots. Main difference is that this plot is for only two variables whereas pairplot can be used for many variables, i.e. there are more cells in the matrix in pairplot whereas there is only one cell with margins on the top. So, one can think of jointplot as pairplot with four cells.
    • This means that if you understand the various arguments that one uses in pairplot, the jointplot looks very similar. Instead of diag_kws, one has to use marginal_kws. Instead of using plot_kws, one needs to use joint_kws

Sample code used to practice

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import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import matplotlib

cars = sns.load_dataset("mpg").dropna()
sns.__version__
matplotlib.__version__

sns.set()
x = np.arange(10)
plt.plot(x, x)
plt.show()
cars.head(10)

sns.kdeplot(data=cars, x="mpg")
plt.show()
sns.kdeplot(data=cars, x="horsepower")
plt.show()
sns.kdeplot(cars.mpg, fill=True)
plt.show()


sns.kdeplot(cars.horsepower, fill=True, bw_adjust=0.2)
plt.show()
sns.kdeplot(cars.horsepower, fill=True, bw_adjust=0.5)
plt.show()
sns.kdeplot(cars.horsepower, fill=True, bw_adjust=2)
plt.show()

sns.kdeplot(data=cars, x="horsepower", y="mpg")
plt.show()

sns.kdeplot(data=cars, x="horsepower", y="mpg", fill=True)
plt.show()

sns.kdeplot(data=cars, x="horsepower", y="mpg", fill=True, cbar=True)
plt.show()

temp = cars[cars.cylinders.isin([4, 8])].copy()
sns.kdeplot(data=temp, x="horsepower", y="mpg", fill=True, cbar=True, hue="cylinders")
plt.show()

sns.kdeplot(data=temp, x="horsepower", y="mpg", fill=True, cbar=False, hue="cylinders")
plt.show()

import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

df = sns.load_dataset("penguins")
df = df.dropna()

df.columns

sns.histplot(data=df, x="bill_length_mm", kde=True)
plt.show()

sns.histplot(data=df, x="bill_length_mm", kde=True, bins=20)
plt.show()

sns.histplot(data=df, x="bill_length_mm", kde=True, bins=[20, 30, 40])
plt.show()

sns.histplot(data=df, x="bill_length_mm", kde=True, binwidth=2)
plt.show()

sns.histplot(data=df, x="bill_length_mm", kde=True, binwidth=2, binrange=[30, 40])
plt.show()

sns.histplot(
    data=df, x="bill_length_mm", kde=True, binwidth=2, binrange=[30, 80], stat="count"
)
plt.show()

sns.histplot(
    data=df, x="bill_length_mm", kde=True, binwidth=2, binrange=[30, 80], stat="density"
)
plt.show()

sns.histplot(
    data=df, x="bill_length_mm", kde=True, binwidth=2, binrange=[30, 80], stat="density"
)
plt.show()

sns.histplot(data=df, x="bill_length_mm", kde=True, stat="probability")
plt.show()

sns.histplot(data=df, x="bill_length_mm", kde=True, cumulative=True, stat="probability")
plt.show()

sns.histplot(data=df, x="bill_length_mm", kde=True, stat="percent")
plt.show()

sns.histplot(data=df, x="bill_length_mm", hue="species", stat="percent")
plt.show()

sns.histplot(data=df, x="bill_length_mm", hue="species", element="step", stat="percent")
plt.show()

sns.histplot(
    data=df, x="bill_length_mm", hue="species", multiple="stack", stat="percent"
)
plt.show()

sns.histplot(
    data=df, x="bill_length_mm", hue="species", multiple="fill", stat="percent"
)
plt.show()

df.columns
sns.set_style("white")
sns.histplot(data=df, x="bill_length_mm", y="bill_depth_mm", cbar=True)
plt.show()

sns.histplot(data=df, x="bill_length_mm", y="bill_depth_mm", hue="species", legend=True)
plt.show()

sns.histplot(x="species", hue="sex", multiple="dodge", data=df, shrink=0.8)
plt.show()

sns.histplot(x="species", hue="sex", multiple="dodge", data=df, shrink=0.8)
plt.show()

sns.histplot(
    x="species", hue="sex", multiple="dodge", data=df, shrink=0.8, palette="bone"
)
plt.show()

sns.histplot(
    x="species", hue="sex", multiple="dodge", data=df, shrink=0.8, color="indigo"
)
plt.show()


sns.histplot(x="species", hue="sex", multiple="dodge", data=df, shrink=0.8, fill=False)
plt.show()

sns.ecdfplot(df.bill_depth_mm)
plt.show()

sns.ecdfplot(data=df, x="bill_depth_mm")
plt.axvline(16, c="black")
plt.show()

sns.ecdfplot(data=df, y="bill_depth_mm")
plt.show()

sns.ecdfplot(data=df, y="bill_depth_mm")
plt.axvline(0.5)
plt.show()

df = sns.load_dataset("tips")

sns.ecdfplot(data=df, x="tip", hue="time")
plt.show()

sns.ecdfplot(data=df, x="tip", hue="day")
plt.show()

sns.ecdfplot(data=df, x="tip", hue="day", stat="count")
plt.show()

sns.ecdfplot(data=df, x="tip", hue="day", stat="count", complementary=True)
plt.show()

sns.ecdfplot(data=df, x="tip", stat="count", complementary=True)
plt.show()

sns.ecdfplot(data=df, x="tip", weights="tip")
plt.axhline(0.5, c="black")
plt.show()

sns.ecdfplot(data=df, x="tip", hue="day")
plt.show()

sns.ecdfplot(data=df, x="tip", hue="day", palette="summer", lw=2)
plt.show()

p = sns.ecdfplot(data=df, x="tip", hue="day", palette="summer", lw=2)
type(p)
p.legend(["Thursday", "Friday", "Saturday", "Sunday"])
plt.show()

cars = sns.load_dataset("mpg").dropna()
cars.columns
sns.set_style("whitegrid")
cars.cylinders.value_counts()
cars = cars[cars.cylinders.isin([4, 6, 8])]
sns.boxplot(data=cars, x="mpg")
plt.show()
cars.mpg.describe()
sns.boxplot(data=cars, x="origin", y="mpg")
plt.show()


sns.boxplot(data=cars, x="origin", y="mpg", hue="cylinders")
plt.show()
cars.model_year
cars.model_year.describe()
cars = cars.assign(newer_model=np.where(cars.model_year > 76, True, False))

sns.boxplot(data=cars, x="origin", y="mpg", hue="newer_model")
plt.show()

sns.boxplot(data=cars, x="origin", y="mpg")
plt.show()


sns.boxplot(data=cars, x="origin", y="mpg")
plt.show()

sns.boxplot(data=cars, x="origin", y="mpg", order=["japan", "usa", "europe"])
plt.show()

sns.boxplot(
    data=cars,
    x="origin",
    y="mpg",
    hue="newer_model",
    order=["japan", "usa", "europe"],
    hue_order=[False, True],
)
plt.show()

sns.boxplot(
    data=cars,
    x="origin",
    y="mpg",
    hue="newer_model",
    order=["japan", "usa", "europe"],
    hue_order=[True, False],
)
plt.show()

sns.boxplot(
    data=cars,
    x="origin",
    y="mpg",
    hue="newer_model",
    order=["japan", "usa", "europe"],
    hue_order=[True, False],
    width=1,
    linewidth=2.5,
)
plt.show()

sns.boxplot(
    data=cars,
    x="origin",
    y="mpg",
    hue="newer_model",
    order=["japan", "usa", "europe"],
    hue_order=[True, False],
    width=1,
    linewidth=2.5,
    whis=3,
)
plt.show()

sns.boxplot(
    data=cars,
    x="origin",
    y="mpg",
    hue="newer_model",
    order=["japan", "usa", "europe"],
    hue_order=[True, False],
    width=0.5,
    linewidth=1,
    whis=1,
    fliersize=3,
    showcaps=False,
)
plt.show()

sns.violinplot(data=cars, x="displacement")
plt.show()

sns.violinplot(data=cars, x="cylinders", y="displacement")
plt.show()

sns.violinplot(data=cars, x="cylinders", y="displacement", hue="origin")
plt.show()
temp = cars[cars.origin.isin(["japan", "europe"])]

sns.violinplot(data=temp, x="cylinders", y="displacement", hue="origin", split=True)
plt.show()

sns.violinplot(
    data=temp,
    x="cylinders",
    y="displacement",
    hue="origin",
    split=True,
    inner="quartiles",
    scale="count",
)
plt.show()

sns.violinplot(
    data=temp,
    x="cylinders",
    y="displacement",
    hue="origin",
    split=True,
    inner="quartiles",
    scale="count",
    scale_hue=False,
)
plt.show()

sns.violinplot(data=temp, x="origin", y="displacement", order=["europe", "japan"])
plt.show()

sns.violinplot(
    data=temp, x="origin", y="displacement", order=["europe", "japan"], linewidth=3
)
plt.show()

sns.violinplot(
    data=temp,
    x="origin",
    y="displacement",
    order=["europe", "japan"],
    linewidth=3,
    bw=0.2,
)
plt.show()


sns.swarmplot(data=cars, x="origin", y="displacement")
plt.show()

sns.swarmplot(data=cars, x="origin", y="horsepower", hue="cylinders", dodge=True)
plt.show()


usa = cars[cars.origin == "usa"]

sns.boxplot(data=usa, x="cylinders", y="horsepower", whis=np.inf)
sns.swarmplot(data=usa, x="cylinders", y="horsepower", color="black", alpha=0.2)
plt.show()


sns.violinplot(data=usa, x="cylinders", y="horsepower", whis=np.inf, scale="width")
sns.swarmplot(data=usa, x="cylinders", y="horsepower", color="black", alpha=0.2)
plt.show()


sns.violinplot(data=usa, x="cylinders", y="horsepower", whis=np.inf, scale="width")

sns.swarmplot(data=usa, x="cylinders", y="horsepower", color="black", alpha=0.2, size=4)
plt.show()


sns.swarmplot(data=usa, x="cylinders", y="horsepower", color="black", alpha=0.2, size=4)
plt.show()


sns.swarmplot(
    data=usa,
    x="cylinders",
    y="horsepower",
    color="black",
    alpha=0.2,
    size=4,
    marker="*",
)
plt.show()

np.random.np.arange(10)

sns.stripplot(data=cars, x="weight")
plt.show()

sns.stripplot(data=cars, x="weight", y="origin", hue="origin")
plt.show()

sns.stripplot(data=cars, x="weight", y="origin", hue="cylinders")
plt.show()

sns.stripplot(data=cars, x="weight", y="origin", hue="cylinders", dodge=True)
plt.show()

sns.stripplot(data=cars, y="weight", x="origin", hue="cylinders", dodge=True)
plt.show()


sns.stripplot(data=cars, y="weight", x="origin", hue="cylinders", jitter=False)
plt.show()

sns.stripplot(data=cars, y="weight", x="origin", hue="cylinders", jitter=0.04)
plt.show()

sns.stripplot(
    data=cars, y="weight", x="origin", hue="cylinders", jitter=0.04, alpha=0.4
)
plt.show()

sns.stripplot(
    data=cars, y="weight", x="origin", hue="cylinders", jitter=0.04, alpha=0.3, size=4
)
plt.show()

sns.stripplot(
    data=cars,
    y="weight",
    x="origin",
    hue="cylinders",
    jitter=0.04,
    alpha=0.3,
    size=4,
    linewidth=1,
)
plt.show()


sns.stripplot(
    data=cars,
    x="weight",
    y="origin",
    hue="cylinders",
    jitter=0.04,
    alpha=0.3,
    size=4,
    linewidth=1,
)
plt.show()

cars.columns
cars.head(1).T
sns.stripplot(
    data=cars, x="weight", y="origin",
)
plt.show()

help(sns.stripplot)

diamonds = sns.load_dataset("diamonds")
diamonds[diamonds.cut.isin(["Premium", "Good"]) & diamonds.color.isin(["D", "F", "J"])]
cars

sns.scatterplot(data=cars, x="cylinders", y="weight", color="purple", hue="newer_model")
plt.show()

sns.scatterplot(
    data=cars, x="cylinders", y="weight", color=["purple", "pink"], hue="newer_model"
)
plt.show()


sns.scatterplot(data=cars, x="cylinders", y="weight", color="#ff028d")
plt.show()

import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

sns.set_style("whitegrid")
df = pd.read_csv("dataset.csv", parse_dates=[3])
df.rename(
    columns={"SystemCodeNumber": "Location", "LastUpdated": "Timestamp"}, inplace=True
)

df = df.assign(Day=df.Timestamp.dt.date)
df = df.assign(Month=df.Timestamp.dt.month)
df = df.assign(Hour=df.Timestamp.dt.hour)

park = df[df.Location.isin(["Broad Street", "NIA South"])]
park.head()

blue, orange, green, red = sns.color_palette()[:4]
sns.set_style("white")
plt.rc("xtick", labelsize=14)
plt.rc("ytick", labelsize=14)
plt.rc("date.autoformatter", days="%b %Y")
import datetime

months = [
    datetime.datetime(2016, 10, 1),
    datetime.datetime(2016, 11, 1),
    datetime.datetime(2016, 12, 1),
]

plt.figure(figsize=(10, 6))
sns.lineplot(data=park, x="Day", y="Occupancy")
plt.xticks(months)
plt.yticks([])
plt.ylim(0, 570)
sns.despine(left=True)
plt.xlabel("")
plt.ylabel("")
plt.tight_layout()
plt.show()

plt.figure(figsize=(10, 6))
sns.lineplot(data=park, x="Day", y="Occupancy")
plt.xticks(months)
plt.yticks([])
plt.ylim(0, 570)
sns.despine(right=True, left=True)
plt.xlabel("")
plt.ylabel("")
plt.tight_layout()
plt.show()

plt.figure(figsize=(12, 8))
sns.lineplot(
    data=park, x="Day", y="Occupancy", hue="Location", palette=["gray", "purple"]
)
plt.xticks(months)
plt.yticks([])
plt.ylim(0, 570)
plt.xlabel("")
plt.ylabel("")
plt.legend([], frameon=False)
plt.tight_layout()
plt.show()


plt.rc("date.autoformatter", day="%b 1st")
plt.figure(figsize=(6, 4))
sns.lineplot(data=park, x="Day", y="Occupancy")
plt.xticks(months)
plt.yticks([])
plt.xlim(pd.datetime(2016, 10, 30), pd.datetime(2016, 11, 6))
plt.ylim(0, 600)
plt.xlabel("")
plt.ylabel("")
plt.show()

plt.rc("date.autoformatter", day="%b 1st")
plt.figure(figsize=(6, 4))
sns.lineplot(data=park, x="Day", y="Occupancy", ci=None)
plt.xticks(months)
plt.yticks([])
plt.xlim(pd.datetime(2016, 10, 30), pd.datetime(2016, 11, 6))
plt.ylim(0, 600)
plt.xlabel("")
plt.ylabel("")
plt.show()

sns.set_style("dark")
months = [
    datetime.datetime(2016, 10, 1),
    datetime.datetime(2016, 11, 1),
    datetime.datetime(2016, 12, 1),
]
plt.rc("date.autoformatter", day="%b %Y")
sns.lineplot(data=park, x="Day", y="Occupancy")
plt.show()


sns.lineplot(data=park, x="Hour", y="Occupancy")
plt.show()

sns.lineplot(x="Hour", y="Occupancy", data=park, n_boot=10)
plt.show()
sns.lineplot(x="Hour", y="Occupancy", data=park, n_boot=100, ci=95)
plt.show()
sns.lineplot(x="Hour", y="Occupancy", data=park, n_boot=100, ci=68)
plt.show()
sns.lineplot(x="Hour", y="Occupancy", data=park, n_boot=100, ci=None)
plt.show()

sns.lineplot(x="Hour", y="Occupancy", data=park, n_boot=100, ci=None, estimator="sum")
plt.show()

import matplotlib.pyplot as plt

xs = np.arange(-(9), (9), 0.01)
ys = -np.square(xs)
cols = ["red", "orange", "yellow", "green", "blue", "purple", "pink"]
cols_labels = [i.upper() for i in cols]

for i in range(7):
    sns.lineplot(x=xs, y=ys - 10 * (i - 1), c=cols[i], label=cols_labels[i])
plt.legend()
plt.show()

cols = ["red", "orange", "yellow", "green", "blue", "indigo", "violet"]
cols_labels = [i.upper() for i in cols]

for i in range(7):
    sns.lineplot(x=xs, y=ys - 10 * (i - 1), c=cols[i], label=cols_labels[i])
plt.legend()
plt.show()


cols = ["red", "orange", "yellow", "green", "blue", "indigo", "violet"]
cols_labels = [i.upper() for i in cols]

for i in range(7):
    sns.lineplot(
        x=xs, y=ys - 10 * (i - 1), color=cols[i], label=cols_labels[i], lw=10, alpha=0.3
    )
plt.legend()
plt.show()


diamonds = sns.load_dataset("diamonds")
diamonds.shape
diamonds = (
    diamonds[
        diamonds.cut.isin(["Premium", "Good"]) & diamonds.color.isin(["D", "F", "J"])
    ]
    .sample(n=100, random_state=22)
    .copy()
)
diamonds.cut.value_counts()
diamonds.cut = diamonds.cut.astype("str")
sns.set_style("white")
plt.rc("xtick", labelsize=14)
plt.rc("ytick", labelsize=14)


diamonds.columns

sns.scatterplot(data=diamonds, x="carat", y="price")
plt.show()

sns.scatterplot(data=diamonds, x="carat", y="price", hue="cut")
plt.show()

sns.scatterplot(data=diamonds, x="carat", y="price", hue="cut", palette=["red", "blue"])
plt.show()

sns.scatterplot(
    data=diamonds,
    x="carat",
    y="price",
    hue="cut",
    palette=["red", "blue"],
    hue_order=["Good", "Premium"],
)
plt.show()

sns.scatterplot(
    data=diamonds,
    x="carat",
    y="price",
    hue="cut",
    palette=["red", "blue"],
    hue_order=["Good", "Premium"],
    s=2,
)
plt.show()

sns.scatterplot(data=diamonds, x="carat", y="price", size="cut", sizes=[10, 50])
plt.show()

sns.scatterplot(
    data=diamonds, x="carat", y="price", size="cut", sizes=[10, 50], marker="*"
)
plt.show()

import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

diamonds = sns.load_dataset("diamonds")
diamonds.shape

diamonds = diamonds.sample(n=200, random_state=1200)
plt.rc("xtick", labelsize=12)
plt.rc("ytick", labelsize=12)


temp = sns.regplot(data=diamonds, x="carat", y="price")
temp.axes.set_title("test plot", fontsize=20)
temp.axes.set_xlabel("x", fontsize=14)
temp.axes.set_ylabel("y", fontsize=14)
plt.show()


temp = sns.regplot(data=diamonds, x="carat", y="price", fit_reg=False)
temp.axes.set_title("test plot", fontsize=20)
temp.axes.set_xlabel("x", fontsize=14)
temp.axes.set_ylabel("y", fontsize=14)
plt.show()


temp = sns.regplot(data=diamonds, x="carat", y="price", scatter=False)
temp.axes.set_title("test plot", fontsize=20)
temp.axes.set_xlabel("x", fontsize=14)
temp.axes.set_ylabel("y", fontsize=14)
plt.show()

temp = sns.regplot(data=diamonds, x="carat", y="price", ci=None)
temp.axes.set_title("test plot", fontsize=20)
temp.axes.set_xlabel("x", fontsize=14)
temp.axes.set_ylabel("y", fontsize=14)
plt.show()


cut_map = {
    'Fair':1,
    'Good':2,
    'Very Good':3,
    'Premium':4,
    'Ideal':5
}
diamonds = diamonds.assign(cut_value =diamonds.cut.map(cut_map))

diamonds.cut_value = diamonds.cut_value.astype('int')
.value_counts()

diamonds.cut_value.as_ordered()
sns.regplot(x='cut_value', y = 'price', data = diamonds, x_jitter=0.1)
plt.show()


sns.regplot(x='cut_value', y = 'price', data = diamonds, x_jitter=0.1, x_estimator=np.mean)
plt.show()


sns.regplot(x='carat', y = 'price', data = diamonds, order= 3)
plt.show()

sns.regplot(x='carat', y = 'price', data = diamonds, robust=True)
plt.show()


sns.regplot(x='carat', y = 'price', data = diamonds, robust=True,
            scatter_kws = {'s':100, 'alpha':0.5, 'color':'lightgray'})
plt.show()


sns.regplot(x='carat', y = 'price', data = diamonds,
            scatter_kws = {'s':100, 'alpha':0.5, 'color':'lightgray'})
plt.show()


sns.regplot(x='carat', y = 'price', data = diamonds,
            ci=None,
            line_kws = {'lw':4, 'color':'black', 'linestyle':'-.'})
plt.show()


temp = diamonds[diamonds.color.isin(['E','J'])].copy()
temp.color = temp.color.astype('str')

p = sns.lmplot(x ='carat', y = 'price', data = temp,
               hue='color', order =2, palette=['green','orange'])
p._legend.remove()
plt.legend(fontsize=16)
plt.show()

sns.jointplot(x ='carat', y = 'price', data = diamonds,
             kind = 'reg', color = 'purple')
plt.show()

sns.pairplot( data = diamonds[['carat','depth','price']],
             kind = 'reg')
plt.show()

import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

sns.set_theme("")
vals = np.random.choice(np.arange(100), 20).reshape(4, -1)
df = pd.DataFrame(vals)
df.index = "A B C D".split()
df.columns = "Mon Tue Wed Thu Fri".split()

sns.heatmap(
    df, cbar=True, annot=True,
)
plt.show()

help(sns.heatmap)
sns.heatmap(
    df,
    cbar=True,
    annot=True,
    vmin=0,
    square=True,
    annot_kws={"fontsize": 14, "fontweight": "bold", "color": "black"},
)
plt.tick_params(
    which="both",
    bottom=False,
    top=False,
    right=False,
    left=False,
    labelbottom=False,
    labeltop=True,
)
plt.tight_layout()
plt.show()


sns.heatmap(
    df,
    cbar=True,
    annot=True,
    vmin=0,
    square=True,
    annot_kws={"fontsize": 14, "fontweight": "bold", "color": "black"},
    linewidth=1,
    linecolor="gray",
)
plt.tight_layout()
plt.show()


sns.heatmap(
    df,
    cbar=True,
    annot=True,
    vmin=0,
    square=True,
    annot_kws={"fontsize": 14, "fontweight": "bold", "color": "black"},
    linewidth=1,
    linecolor="lightgray",
)
plt.tight_layout()
plt.show()

sns.heatmap(
    df,
    cbar=True,
    annot=True,
    vmin=0,
    square=True,
    annot_kws={"fontsize": 14, "fontweight": "bold", "color": "black"},
    linewidth=1,
    linecolor="lightgray",
    cmap="Blues",
)
plt.tight_layout()
plt.show()


sns.heatmap(
    df,
    cbar=True,
    annot=True,
    vmin=0,
    square=True,
    fmt=".0f",
    annot_kws={"fontsize": 14, "fontweight": "bold", "color": "black"},
    linewidth=1,
    linecolor="lightgray",
    cmap="Blues",
)
plt.tight_layout()
plt.show()
df

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

sns.set_style("white")

penguins = sns.load_dataset("penguins")
pen_ex = penguins[penguins.species.isin(["Adelie", "Chinstrap"])]
pen_ex.body_mass_g.describe()

g = sns.FacetGrid(data=pen_ex, row="sex", col="species", aspect=1.75)
g.map_dataframe(sns.histplot, x="body_mass_g", binwidth=200, color="purple")
g.set_titles(row_template="{row_name}", col_template="{col_name} Penguins")
g.set_axis_labels("Body Mass(g)", "Count")
plt.tight_layout()
plt.rc("xtick", labelsize=10)
plt.rc("ytick", labelsize=10)
plt.rc("axes", labelsize=15)
plt.show()


g = sns.FacetGrid(data=penguins, columns="island", aspect=1.75)
g.map_dataframe(sns.histplot, x="body_mass_g", binwidth=200, color="purple")
g.set_titles(row_template="{row_name}", col_template="{col_name} Penguins")
g.set_axis_labels("Body Mass(g)", "Count")
plt.tight_layout()
plt.rc("xtick", labelsize=10)
plt.rc("ytick", labelsize=10)
plt.rc("axes", labelsize=15)
plt.show()

penguins.columns
g = sns.FacetGrid(data=penguins, col="island", hue="species")
g.map_dataframe(sns.scatterplot, x="bill_depth_mm", y="bill_length_mm")
g.add_legend()
plt.show()


g = sns.FacetGrid(data=penguins, col="island", hue="species", palette="prism")
g.map_dataframe(sns.scatterplot, x="bill_depth_mm", y="bill_length_mm")
g.add_legend()
plt.show()


cars = sns.load_dataset("mpg").dropna()
sns.displot(data=cars, x="weight", kde=True)
plt.show()

sns.displot(data=cars, x="weight", kind="kde")
plt.show()

sns.displot(data=cars, x="weight", kind="ecdf", rug=True)
plt.show()

sns.displot(data=cars, x="weight", kind="hist", rug=True, hue="origin")
plt.show()

sns.displot(data=cars, x="weight", kind="kde", rug=True, hue="origin")
plt.show()

sns.displot(data=cars, x="weight", y="horsepower")
plt.show()

sns.displot(data=cars, x="weight", y="horsepower", kind="kde")
plt.show()

sns.displot(data=cars, x="weight", y="horsepower", kind="kde", rug=True)
plt.show()

sns.displot(data=cars, x="weight", hue="origin", col="origin")
plt.show()

g = sns.FacetGrid(data=cars, col="origin")
g.map_dataframe(sns.histplot, x="weight")
plt.show()

sns.displot(data=cars, x="weight", hue="origin", col="origin", kind="kde", rug=True)
plt.show()

sns.displot(data=cars, x="weight", col="origin", row="cylinders")
plt.show()

sns.displot(data=cars, x="weight", hue="origin", col="origin", palette="bone")
plt.show()

sns.displot(data=cars, x="weight", hue="origin", col="origin", palette="bone", aspect=1)
plt.show()


sns.displot(
    data=cars, x="weight", col="origin", aspect=1, rug=True, rug_kws={"height": 0.1}
)
plt.show()

g = sns.displot(
    data=cars, x="weight", hue="origin", col="origin", palette="bone", aspect=1
)
g.axes_dict.items()
plt.show()

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

sns.set_style("white")
df = sns.load_dataset("tips")
sns.regplot(x="total_bill", y="tip", data=df)
plt.show()

sns.lmplot(x="total_bill", y="tip", data=df)
plt.show()

sns.lmplot(y="size", x="tip", data=df)
plt.show()

sns.lmplot(x="size", y="tip", data=df)
plt.show()
anscombe = sns.load_dataset("anscombe")
sns.lmplot(
    x="x",
    y="y",
    data=anscombe[anscombe.dataset == "I"],
    scatter_kws={"s": 60},
    ci=False,
)
plt.show()
sns.lmplot(
    x="x",
    y="y",
    data=anscombe[anscombe.dataset == "II"],
    scatter_kws={"s": 60},
    ci=False,
)
plt.show()

sns.lmplot(
    x="x",
    y="y",
    data=anscombe[anscombe.dataset == "II"],
    order=2,
    scatter_kws={"s": 60},
    ci=False,
)
plt.show()

sns.lmplot(
    x="x",
    y="y",
    data=anscombe[anscombe.dataset == "III"],
    order=2,
    scatter_kws={"s": 60},
    ci=False,
)
plt.show()

sns.lmplot(
    x="x",
    y="y",
    data=anscombe[anscombe.dataset == "III"],
    robust=True,
    scatter_kws={"s": 60},
    ci=False,
)
plt.show()

df = df.assign(big_tip=(df.tip / df.total_bill) > 0.15)

sns.lmplot(x="total_bill", y="big_tip", data=df)
plt.show()

sns.lmplot(x="total_bill", y="big_tip", data=df, logistic=True)
plt.show()

sns.lmplot(x="total_bill", y="big_tip", data=df, logistic=True, y_jitter=0.4)
plt.show()

sns.lmplot(x="total_bill", y="big_tip", data=df, logistic=True, y_jitter=0.1)
plt.show()


sns.lmplot(
    x="total_bill",
    y="tip",
    data=df,
    lowess=True,
    y_jitter=0.1,
    line_kws={"color": "red"},
)
plt.show()

sns.residplot(x="x", y="y", data=anscombe[anscombe.dataset == "I"])
plt.show()

sns.residplot(x="x", y="y", data=anscombe[anscombe.dataset == "II"])
plt.show()

import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt

penguins = sns.load_dataset("penguins")
penguins.columns
sns.barplot(data=penguins, x="species", y="body_mass_g")
plt.show()
penguins.species.value_counts()
penguins.groupby("species")["body_mass_g"].mean()


sns.barplot(data=penguins, x="species", y="body_mass_g", ci=None)
plt.show()
penguins.columns
sns.barplot(data=penguins, x="species", y="body_mass_g", hue="island", ci=None)
plt.show()

sns.barplot(
    data=penguins, x="species", y="body_mass_g", hue="island", ci=None, estimator=np.std
)
plt.show()

sns.barplot(data=penguins, x="species", y="body_mass_g", estimator=np.max)
plt.show()
penguins.groupby("species")["body_mass_g"].max()

sns.barplot(
    data=penguins,
    x="species",
    y="body_mass_g",
    estimator=np.max,
    order=["Adelie", "Chinstrap", "Gentoo"][::-1],
)
plt.show()
penguins.groupby("species")["body_mass_g"].max().index.tolist()
x = ["Adelie", "Chinstrap", "Gentoo"]
penguins.columns
sns.barplot(
    data=penguins, x="species", y="body_mass_g", estimator=np.max, hue="sex",
)
plt.show()

sns.barplot(
    data=penguins,
    x="species",
    y="body_mass_g",
    estimator=np.max,
    hue="sex",
    hue_order=["Male", "Female"],
    order=["Adelie", "Chinstrap", "Gentoo"][::-1],
)
plt.show()

sns.barplot(
    data=penguins,
    x="species",
    y="body_mass_g",
    estimator=np.max,
    hue="sex",
    hue_order=["Male", "Female"],
    order=["Adelie", "Chinstrap", "Gentoo"][::-1],
    palette="hot",
    edgecolor="aliceblue",
    lw=2,
)
plt.show()

penguins_sample = (
    penguins.dropna().groupby(["species", "sex"]).sample(3, random_state=10)
)

penguins_sample
temp = penguins_sample.groupby(["species", "sex"])["body_mass_g"].sum().reset_index()
sns.barplot(
    data=temp, x="species", y="body_mass_g", hue="sex", estimator=np.sum, ci=False,
)
plt.show()
temp = penguins_sample.groupby(["species", "sex"])["body_mass_g"].sum().unstack()

temp.plot(kind="bar", stacked=True)
plt.show()

diamonds = sns.load_dataset("diamonds")
sns.countplot(data=diamonds, x="color")
plt.show()

diamonds.cut.cat.categories
sns.countplot(
    data=diamonds, x="color", order=diamonds.color.value_counts().index.tolist()
)
plt.show()

sns.countplot(
    data=diamonds,
    x="color",
    hue="clarity",
    order=diamonds.color.value_counts().index.tolist(),
)
plt.show()


sns.countplot(
    data=diamonds,
    x="color",
    color="red",
    order=diamonds.color.value_counts().index.tolist(),
)
plt.show()

import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")
tips.columns
tips.dtypes

g = sns.pairplot(tips)
plt.show()

g = sns.pairplot(tips, kind="kde")

sns.pairplot(tips, diag_kind="kde")
plt.show()

sns.pairplot(tips, diag_kind="kde", corner=True)
plt.show()

sns.pairplot(tips, diag_kind="kde", markers="^", hue="sex")
plt.show()

sns.pairplot(tips, diag_kind="kde", markers="^", hue="sex", palette="bone")
plt.show()

sns.pairplot(tips, diag_kind="kde", markers="^", plot_kws={"color": "black"})
plt.show()

sns.pairplot(
    tips,
    diag_kind="kde",
    kind="reg",
    plot_kws={"line_kws": {"color": "red", "lw": 2}},
    diag_kws={"color": "brown", "lw": 3, "fill": False},
)
plt.show()
sns.kdeplot
help(sns.regplot)
help(sns.pairplot)

g = sns.pairplot(
    tips,
    diag_kind="kde",
    kind="scatter",
    diag_kws={"color": "brown", "lw": 3, "fill": False},
)
g.map_upper(sns.kdeplot, n_levels=6)
plt.show()

g = sns.pairplot(
    tips,
    diag_kind="kde",
    kind="scatter",
    diag_kws={"color": "brown", "lw": 3, "fill": False},
    vars=["total_bill", "tip"],
)
g.map_upper(sns.kdeplot, n_levels=6)
plt.show()

g = sns.pairplot(
    tips,
    diag_kind="kde",
    kind="reg",
    diag_kws={"color": "brown", "lw": 3, "fill": False},
    plot_kws={"ci": None, "scatter_kws": {"s": 1},},
    vars=["total_bill", "tip"],
)
plt.show()

g = sns.pairplot(
    tips,
    hue="sex",
    diag_kind="kde",
    kind="reg",
    diag_kws={"color": "brown", "lw": 3, "fill": False},
    plot_kws={"ci": None, "scatter_kws": {"s": 1},},
    vars=["total_bill", "tip"],
    palette="plasma",
)
plt.show()


geyser = sns.load_dataset("geyser")

geyser
sns.jointplot(data=geyser, x="waiting", y="duration")
plt.show()
g = sns.jointplot(data=geyser)

g = sns.jointplot(data=geyser, x="waiting", y="duration")
g.plot_marginals(sns.rugplot, height=1)
g.ax_joint.set_xlabel("")
g.ax_joint.set_ylabel("")
plt.show()


help(sns.jointplot)
g = sns.jointplot(data=geyser, x="waiting", y="duration", kind="kde")
g.plot_marginals(sns.histplot, height=1)
g.ax_joint.set_xlabel("")
g.ax_joint.set_ylabel("")
plt.show()


g = sns.jointplot(data=geyser, x="waiting", y="duration")
g.plot_marginals(sns.rugplot, height=-0.1, c="red", clip_on=False)
g.plot_joint(sns.kdeplot, c="black", levels=3)

g.ax_joint.set_xlabel("")
g.ax_joint.set_ylabel("")
g.ax_joint.set_xlim(30, 110)
g.ax_joint.set_ylim(0.5, 6)
plt.show()


g = sns.jointplot(data=geyser, x="waiting", y="duration")
g.plot_marginals(sns.rugplot, height=-0.1, c="red", clip_on=False)
g.plot_joint(sns.kdeplot, c="black", levels=3)

g.ax_joint.set_xlabel("")
g.ax_joint.set_ylabel("")
g.ax_joint.set_xlim(30, 110)
g.ax_joint.set_ylim(0.5, 6)
plt.show()


g = sns.jointplot(data=geyser, x="waiting", y="duration", kind="hist")
g.ax_joint.set_xlabel("")
g.ax_joint.set_ylabel("")
g.ax_joint.set_xlim(30, 110)
g.ax_joint.set_ylim(0.5, 6)
plt.show()

g = sns.jointplot(data=geyser, x="waiting", y="duration", kind="hist", hue="kind")
g.ax_joint.set_xlabel("")
g.ax_joint.set_ylabel("")
g.ax_joint.set_xlim(30, 110)
g.ax_joint.set_ylim(0.5, 6)
plt.show()
help(sns.jointplot)
help(sns.pairplot)

What’s my takeaway this time ?

seaborn is a library that allows one to plot univariate, bivariate relationships between various features present as columns in a pandas DataFrame. There are two types of functions in seaborn, one that yields a FacetGrid object and one that yields an axis object. You can choose to use the lower level functions if all you are looking to create, is a single figure. But if you want multiple figures, then displot, regplot and catplot could be very useful

For some one familiar with ggplot2, this is a good python library that will closely reflect grammar of graphics concepts. The fact that it works seamlessly with pandas objects might be a good reason to look at this library and get comfortable using the library.

At a high level, if one thinks of various kinds of visuals that one can display on a 2D plane, they can organized as follows

  • One dimensional plot
    • Histogram
    • ECDF plot
    • KDE
    • Rug plot
    • Bar plot
    • Count plot
  • Two dimensional - Both variables are numeric
    • Scatter
    • Line
    • Regression
    • Heatmap
  • Two dimensional - One numeric and other categorical
    • Box plot
    • Violin plot
    • Swarm plot
    • Strip plot
  • Two dimensional - generic
    • Pair plot
    • Joint plot
  • Embedding facet level graphs, which is by default in ggplot2 type libraries, but there are separate functions in seaborn
    • displot
    • relplot
    • catplot
    • PairGrid plot
    • JointGrid plot

Once you realize that the above covers probably 80pct of your plotting requirements, then all one has to figure out are the styling aspects of a graph such as

  • what should be the height, width and aspect ratio of the visual ?
  • what markers to use ?
  • what should be marker size ?
  • what color palette to use ?
  • what should be the font size ?
  • what line width to use ?
  • what colors to use to show specific sub components of a plot ?
  • If you are showing a rug plot, what should be the height of the rug plot ?
  • should one show a conditional plot ?
  • should one show a conditional and grid plot ?

By spending an hour or two on seaborn, a wonderfully designed library, one can get a good grasp of probably most of the visuals that you would end up either in your exploratory work or your final presentation.