Visualizations with Seaborn

13 minute read

Everything seaborn does to create all kinds of plots is here.

Import Libraries

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

import plotly.plotly as py
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(connected=True)
import plotly.graph_objs as go

import seaborn as sns 
from scipy import stats
from scipy.stats import norm
#so we dont have to write .plot()
%matplotlib inline 
sns.set_style("whitegrid") #possible choices: white, dark, whitegrid, darkgrid, ticks

train = pd.read_csv('train.csv', sep=',')
test = pd.read_csv('test.csv', sep=',')

Dataset from kaggle

SalePrice: - the property’s sale price in dollars. This is the target variable that you’re trying to predict.
MSSubClass: The building class
MSZoning: The general zoning classification
LotFrontage: Linear feet of street connected to property
LotArea: Lot size in square feet
Street: Type of road access
Alley: Type of alley access
LotShape: General shape of property
LandContour: Flatness of the property
Utilities: Type of utilities available
LotConfig: Lot configuration
LandSlope: Slope of property
Neighborhood: Physical locations within Ames city limits
Condition1: Proximity to main road or railroad
Condition2: Proximity to main road or railroad (if a second is present)
BldgType: Type of dwelling
HouseStyle: Style of dwelling
OverallQual: Overall material and finish quality
OverallCond: Overall condition rating
YearBuilt: Original construction date
YearRemodAdd: Remodel date
RoofStyle: Type of roof
RoofMatl: Roof material
Exterior1st: Exterior covering on house
Exterior2nd: Exterior covering on house (if more than one material)
MasVnrType: Masonry veneer type
MasVnrArea: Masonry veneer area in square feet
ExterQual: Exterior material quality
ExterCond: Present condition of the material on the exterior
Foundation: Type of foundation
BsmtQual: Height of the basement
BsmtCond: General condition of the basement
BsmtExposure: Walkout or garden level basement walls
BsmtFinType1: Quality of basement finished area
BsmtFinSF1: Type 1 finished square feet
BsmtFinType2: Quality of second finished area (if present)
BsmtFinSF2: Type 2 finished square feet
BsmtUnfSF: Unfinished square feet of basement area
TotalBsmtSF: Total square feet of basement area
Heating: Type of heating
HeatingQC: Heating quality and condition
CentralAir: Central air conditioning
Electrical: Electrical system
1stFlrSF: First Floor square feet
2ndFlrSF: Second floor square feet
LowQualFinSF: Low quality finished square feet (all floors)
GrLivArea: Above grade (ground) living area square feet
BsmtFullBath: Basement full bathrooms
BsmtHalfBath: Basement half bathrooms
FullBath: Full bathrooms above grade
HalfBath: Half baths above grade
Bedroom: Number of bedrooms above basement level
Kitchen: Number of kitchens
KitchenQual: Kitchen quality
TotRmsAbvGrd: Total rooms above grade (does not include bathrooms)
Functional: Home functionality rating
Fireplaces: Number of fireplaces
FireplaceQu: Fireplace quality
GarageType: Garage location
GarageYrBlt: Year garage was built
GarageFinish: Interior finish of the garage
GarageCars: Size of garage in car capacity
GarageArea: Size of garage in square feet
GarageQual: Garage quality
GarageCond: Garage condition
PavedDrive: Paved driveway
WoodDeckSF: Wood deck area in square feet
OpenPorchSF: Open porch area in square feet
EnclosedPorch: Enclosed porch area in square feet
3SsnPorch: Three season porch area in square feet
ScreenPorch: Screen porch area in square feet
PoolArea: Pool area in square feet
PoolQC: Pool quality
Fence: Fence quality
MiscFeature: Miscellaneous feature not covered in other categories
MiscVal: Value of miscellaneous feature
MoSold: Month Sold
YrSold: Year Sold
SaleType: Type of sale
SaleCondition: Condition of sale

Univariate analysis is the simplest form of data analysis where the data being analyzed contains only one variable. Since it’s a single variable it doesn’t deal with causes or relationships. The main purpose of univariate analysis is to describe the data and find patterns that exist within it

Histogram

Flexibly plot a univariate distribution of observations. https://seaborn.pydata.org/generated/seaborn.distplot.html

fig, ax = plt.subplots(figsize=(15, 5))

sns.distplot(train['SalePrice'])

ax.set_title('Sale Price Distribution', fontsize = 15, loc='center')
ax.set_xlabel('Sale Price', fontsize = 13)
plt.tick_params(axis='x', which='major', labelsize=12)
ax.yaxis.tick_left() # where the y axis marks will be

png

We see that the SalePrice:

Deviate from the normal distribution.
Have appreciable positive skewness.
Show peakedness.

Skewness

It is the degree of distortion from the symmetrical bell curve or the normal distribution. It measures the lack of symmetry in data distribution. It differentiates extreme values in one versus the other tail. A symmetrical distribution will have a skewness of 0.

Positive Skewness means when the tail on the right side of the distribution is longer or fatter. The mean and median will be greater than the mode.
Negative Skewness is when the tail of the left side of the distribution is longer or fatter than the tail on the right side. The mean and median will be less than the mode.

skewness

The rule of thumb seems to be:

If the skewness is between -0.5 and 0.5, the data are fairly symmetrical.
If the skewness is between -1 and -0.5(negatively skewed) or between 0.5 and 1(positively skewed), the data are moderately skewed.
If the skewness is less than -1(negatively skewed) or greater than 1(positively skewed), the data are highly skewed.

In case of positive skewness, log transformations usually works well. The probability plot is a graphical technique for assessing whether or not a data set follows a given distribution such as the normal.

Kurtosis

It is all about the tails of the distribution — not the peakedness or flatness. It is used to describe the extreme values in one versus the other tail. It is actually the measure of outliers present in the distribution.

High kurtosis in a data set is an indicator that data has heavy tails or outliers. If there is a high kurtosis, then, we need to investigate why do we have so many outliers. It indicates a lot of things, maybe wrong data entry or other things.
Low kurtosis in a data set is an indicator that data has light tails or lack of outliers. If we get low kurtosis(too good to be true), then also we need to investigate and trim the dataset of unwanted results.

kurtosis

Mesokurtic: This distribution has kurtosis statistic similar to that of the normal distribution. It means that the extreme values of the distribution are similar to that of a normal distribution characteristic. This definition is used so that the standard normal distribution has a kurtosis of three.
Leptokurtic (Kurtosis > 3): Distribution is longer, tails are fatter. Peak is higher and sharper than Mesokurtic, which means that data are heavy-tailed or profusion of outliers. Outliers stretch the horizontal axis of the histogram graph, which makes the bulk of the data appear in a narrow (“skinny”) vertical range, thereby giving the “skinniness” of a leptokurtic distribution.
Platykurtic: (Kurtosis < 3): Distribution is shorter, tails are thinner than the normal distribution. The peak is lower and broader than Mesokurtic, which means that data are light-tailed or lack of outliers. The reason for this is because the extreme values are less than that of the normal distribution.

print("Skewness: {:.3f}".format(train['SalePrice'].skew()))
print("Kurtosis: {:.3f}". format(train['SalePrice'].kurt()))

Skewness: 1.883
Kurtosis: 6.536

fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))
sns.distplot(train['SalePrice'], fit=norm, ax=ax[0])
stats.probplot(train['SalePrice'], plot=plt)
ax[0].set_xlabel('Sale Price Distribution', fontsize = 13)
ax[1].set_xlabel('Sale Price Propability', fontsize = 13)
ax[1].yaxis.tick_right() # where the y axis marks will be

png

train['SalePrice'] = np.log(train['SalePrice'])

fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))
sns.distplot(train['SalePrice'], fit=norm, ax=ax[0])
stats.probplot(train['SalePrice'], plot=plt)
ax[0].set_xlabel('Sale Price Distribution', fontsize = 13)
ax[1].set_xlabel('Sale Price Propability', fontsize = 13)
ax[1].yaxis.tick_right() # where the y axis marks will be

png

#Let's load again the dataset
train = pd.read_csv('train.csv', sep=',')

KDE Plot

Fit and plot a univariate or bivariate kernel density estimate. https://seaborn.pydata.org/generated/seaborn.kdeplot.html

fig, ax = plt.subplots(figsize=(15, 5))

sns.kdeplot(train['SalePrice'], shade=True)

ax.set_title('Sale Price Distribution', fontsize = 15, loc='center')
ax.set_xlabel('Sale Price', fontsize = 13)
plt.tick_params(axis='x', which='major', labelsize=12)
ax.yaxis.tick_left() # where the y axis marks will be

png

Box Plot

Draw a box plot to show distributions with respect to categories. https://seaborn.pydata.org/generated/seaborn.boxplot.html

fig, ax = plt.subplots(figsize=(15, 5))

sns.boxplot(train['SalePrice'], orient = 'v')

ax.set_title('Sale Price Distribution', fontsize = 15, loc='center')
ax.set_xlabel('Sale Price', fontsize = 13)
plt.tick_params(axis='x', which='major', labelsize=12)
ax.yaxis.tick_left() # where the y axis marks will be

png

Count Plot

Show the counts of observations in each categorical bin using bars. A count plot can be thought of as a histogram across a categorical, instead of quantitative, variable.

https://seaborn.pydata.org/generated/seaborn.countplot.html

fig, ax = plt.subplots(figsize=(15,6))

sns.countplot(train['YearBuilt'])

plt.xticks(rotation=90)
ax.set_xlabel('Year Built', fontsize = 12)
plt.tick_params(axis='x', which='major', labelsize=8)
ax.yaxis.tick_left() # where the y axis marks will be

png

Pie Chart

f,ax=plt.subplots(figsize=(8,8))
train['GarageCars'].value_counts().plot(kind='pie')

ax.set_ylabel('Garage Cars', fontsize = 12)

<matplotlib.text.Text at 0x1fae90a93c8>

png

Bivariate Analysis

Bivariate analysis is used to find out if there is a relationship between two different variables.

Continious

Scatter Plot

Draw a scatter plot with possibility of several semantic groupings. https://seaborn.pydata.org/generated/seaborn.scatterplot.html

fig, ax = plt.subplots(figsize=(15, 6))

sns.scatterplot(train['GrLivArea'], train['SalePrice'])

ax.set_title('Sale Price per Live Area', fontsize = 15, loc='center')
ax.set_xlabel('Livινg Area', fontsize = 13)
ax.set_ylabel('Sale Price', fontsize = 13)
plt.tick_params(axis='y', which='major', labelsize=12)
plt.tick_params(axis='x', which='major', labelsize=12)
ax.yaxis.tick_left() # where the y axis marks will be

png

Join Plot (scatter)

Draw a plot of two variables with bivariate and univariate graphs. https://seaborn.pydata.org/generated/seaborn.jointplot.html

sns.jointplot(train['GrLivArea'], train['SalePrice'], height=8, kind='scatter')

<seaborn.axisgrid.JointGrid at 0x1fae90bb0b8>

png

Join Plot (reg) or lm Plot

#alternative: sns.lmplot(x='GrLivArea', y='SalePrice', data=train)
sns.jointplot(train['GrLivArea'], train['SalePrice'], height=8, kind='reg')

<seaborn.axisgrid.JointGrid at 0x1fae7a4c3c8>

png

Join Plot (kde) or kde Plot

#alternative: sns.kdeplot(train['GrLivArea'], train['SalePrice'], shade=True)
sns.jointplot(train['GrLivArea'], train['SalePrice'], height=8, kind='kde')

c:\users\jim\appdata\local\programs\python\python35\lib\site-packages\numpy\ma\core.py:6461: MaskedArrayFutureWarning:

In the future the default for ma.minimum.reduce will be axis=0, not the current None, to match np.minimum.reduce. Explicitly pass 0 or None to silence this warning.

c:\users\jim\appdata\local\programs\python\python35\lib\site-packages\numpy\ma\core.py:6461: MaskedArrayFutureWarning:

In the future the default for ma.maximum.reduce will be axis=0, not the current None, to match np.maximum.reduce. Explicitly pass 0 or None to silence this warning.

<seaborn.axisgrid.JointGrid at 0x1fae7b7ba90>

png

Join Plot (hex)

sns.jointplot(train['GrLivArea'], train['SalePrice'], height=8, kind='hex')

<seaborn.axisgrid.JointGrid at 0x1fae7dba278>

png

Line Plot (alternatives: pointplot, relplot)

Draw a line plot with possibility of several semantic groupings.

The relationship between x and y can be shown for different subsets of the data using the hue, size, and style parameters. These parameters control what visual semantics are used to identify the different subsets. It is possible to show up to three dimensions independently by using all three semantic types, but this style of plot can be hard to interpret and is often ineffective. Using redundant semantics (i.e. both hue and style for the same variable) can be helpful for making graphics more accessible.

https://seaborn.pydata.org/generated/seaborn.lineplot.html

fig, ax = plt.subplots(figsize=(15, 6))

sns.lineplot(train['YearBuilt'], train['SalePrice'], ci='sd' ) #'sd' shows the std

ax.set_title('Sale Price per Year Built', fontsize = 15, loc='center')
ax.set_xlabel('Year Built', fontsize = 13)
ax.set_ylabel('Sale Price', fontsize = 13)
plt.tick_params(axis='y', which='major', labelsize=12)
plt.tick_params(axis='x', which='major', labelsize=12)
ax.yaxis.tick_left() # where the y axis marks will be

png

Categorical

Bar Plot

Show point estimates and confidence intervals as rectangular bars.

A bar plot represents an estimate of central tendency for a numeric variable with the height of each rectangle and provides some indication of the uncertainty around that estimate using error bars. Bar plots include 0 in the quantitative axis range, and they are a good choice when 0 is a meaningful value for the quantitative variable, and you want to make comparisons against it.

https://seaborn.pydata.org/generated/seaborn.barplot.html

fig, ax = plt.subplots(figsize=(10,6))

sns.barplot(train['OverallQual'], train['SalePrice'])

ax.set_title('Sale Price per Year Built', fontsize = 15, loc='center')
ax.set_xlabel('Year Built', fontsize = 13)
ax.set_ylabel('Overall Quality', fontsize = 13)
plt.tick_params(axis='y', which='major', labelsize=12)
plt.tick_params(axis='x', which='major', labelsize=12)
ax.yaxis.tick_left() # where the y axis marks will be

png

Boxplot

https://seaborn.pydata.org/generated/seaborn.boxplot.html

fig, ax = plt.subplots(figsize=(15, 6))

sns.boxplot(train['OverallQual'], train['SalePrice'])

ax.set_title('Sale Price per Overal Quality', fontsize = 15, loc='center')
ax.set_xlabel('Overal Quality', fontsize = 13)
ax.set_ylabel('Sale Price', fontsize = 13)
plt.tick_params(axis='y', which='major', labelsize=12)
plt.tick_params(axis='x', which='major', labelsize=12)
ax.yaxis.tick_left() # where the y axis marks will be

png

Violin Plot

Draw a combination of boxplot and kernel density estimate.

A violin plot plays a similar role as a box and whisker plot. It shows the distribution of quantitative data across several levels of one (or more) categorical variables such that those distributions can be compared. Unlike a box plot, in which all of the plot components correspond to actual datapoints, the violin plot features a kernel density estimation of the underlying distribution.

This can be an effective and attractive way to show multiple distributions of data at once, but keep in mind that the estimation procedure is influenced by the sample size, and violins for relatively small samples might look misleadingly smooth.

https://seaborn.pydata.org/generated/seaborn.violinplot.html

fig, ax = plt.subplots(figsize=(15, 6))

sns.violinplot(train['OverallQual'], train['SalePrice'])

ax.set_title('Sale Price per Overal Quality', fontsize = 15, loc='center')
ax.set_xlabel('Overal Quality', fontsize = 13)
ax.set_ylabel('Sale Price', fontsize = 13)
plt.tick_params(axis='y', which='major', labelsize=12)
plt.tick_params(axis='x', which='major', labelsize=12)
ax.yaxis.tick_left() # where the y axis marks will be

png

Boxen Plot

Draw an enhanced box plot for larger datasets.

This style of plot was originally named a “letter value” plot because it shows a large number of quantiles that are defined as “letter values”. It is similar to a box plot in plotting a nonparametric representation of a distribution in which all features correspond to actual observations. By plotting more quantiles, it provides more information about the shape of the distribution, particularly in the tails.

https://seaborn.pydata.org/generated/seaborn.boxenplot.html#seaborn.boxenplot

fig, ax = plt.subplots(figsize=(15, 6))

sns.boxenplot(train['OverallQual'], train['SalePrice'])

ax.set_title('Sale Price per Overal Quality', fontsize = 15, loc='center')
ax.set_xlabel('Overal Quality', fontsize = 13)
ax.set_ylabel('Sale Price', fontsize = 13)
plt.tick_params(axis='y', which='major', labelsize=12)
plt.tick_params(axis='x', which='major', labelsize=12)
ax.yaxis.tick_left() # where the y axis marks will be

png

Strip Plot on Box Plot

A strip plot can be drawn on its own, but it is also a good complement to a box or violin plot in cases where you want to show all observations along with some representation of the underlying distribution.

https://seaborn.pydata.org/generated/seaborn.stripplot.html

fig, ax = plt.subplots(figsize=(15, 6))

sns.boxplot(train['OverallQual'], train['SalePrice']) #or violinplot
sns.stripplot(train['OverallQual'], train['SalePrice'], color=".3")

ax.set_title('Sale Price per Overal Quality', fontsize = 15, loc='center')
ax.set_xlabel('Overal Quality', fontsize = 13)
ax.set_ylabel('Sale Price', fontsize = 13)
plt.tick_params(axis='y', which='major', labelsize=12)
plt.tick_params(axis='x', which='major', labelsize=12)
ax.yaxis.tick_left() # where the y axis marks will be

png

Multivariate Analysis

Correlation Heatmap

corrmat = train.corr()
fig, ax = plt.subplots(figsize=(15,8))
sns.heatmap(corrmat, vmax=.8, square=True);

png

fig, ax = plt.subplots(figsize=(15,8))
k = 10 #number of variables for heatmap
cols = corrmat.nlargest(k, 'SalePrice')['SalePrice'].index
cm = np.corrcoef(train[cols].values.T)
sns.set(font_scale=1.25)
sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)

<matplotlib.axes._subplots.AxesSubplot at 0x1faea1bb4e0>

png

Pairplot

cols = ['SalePrice', 'OverallQual', 'GrLivArea', 'GarageCars', 'TotalBsmtSF', 'FullBath', 'YearBuilt']
sns.pairplot(train[cols], height = 2.5)

<seaborn.axisgrid.PairGrid at 0x1faea159a90>

png

3 Variables with Scatter Plot

fig, ax = plt.subplots(figsize=(15,6))

sns.scatterplot(train['GrLivArea'], train['SalePrice'], hue=train['OverallQual'], palette='Reds', size=train['OverallQual'],
               legend="full")

ax.set_title('Sale Price per Living Area grouped by Overall Quality', fontsize = 15, loc='center')
ax.set_xlabel('Living Area', fontsize = 13)
ax.set_ylabel('Sale Price', fontsize = 13)
plt.tick_params(axis='y', which='major', labelsize=12)
plt.tick_params(axis='x', which='major', labelsize=12)
ax.yaxis.tick_left() # where the y axis marks will be

png

4 Variables with Scatter Plot

fig, ax = plt.subplots(figsize=(15,6))

sns.scatterplot(train['GrLivArea'], train['SalePrice'], hue=train['OverallQual'], palette='Blues', style=train['GarageCars'],
               size=train['OverallQual'])

ax.set_title('Sale Price per Living Area grouped by Overall Quality and Garage Cars', fontsize = 15, loc='center')
ax.set_xlabel('Living Area', fontsize = 13)
ax.set_ylabel('Sale Price', fontsize = 13)
plt.tick_params(axis='y', which='major', labelsize=12)
plt.tick_params(axis='x', which='major', labelsize=12)
ax.yaxis.tick_left() # where the y axis marks will be

png

3 Variables with Line Plot

fig, ax = plt.subplots(figsize=(15,6))

sns.lineplot(train['OverallQual'], train['SalePrice'], ci='sd' ,hue=train['GarageCars'], palette='RdYlBu',
            style=train['GarageCars'], markers=True, dashes=False)

ax.set_title('Sale Price per Overall Quality and Garage Cars', fontsize = 15, loc='center')
ax.set_xlabel('Overall Quality', fontsize = 13)
ax.set_ylabel('Sale Price', fontsize = 13)
plt.tick_params(axis='y', which='major', labelsize=12)
plt.tick_params(axis='x', which='major', labelsize=12)
ax.yaxis.tick_left() # where the y axis marks will be

png

3 Variables with Bar Plot

fig, ax = plt.subplots(figsize=(15,6))
sns.barplot(train['OverallQual'], train['SalePrice'], hue=train['GarageCars'])
ax.legend(loc='upper left')

ax.set_title('Sale Price per Overall Quality and Garage Cars', fontsize = 15, loc='center')
ax.set_xlabel('Overall Quality', fontsize = 13)
ax.set_ylabel('Sale Price', fontsize = 13)
plt.tick_params(axis='y', which='major', labelsize=12)
plt.tick_params(axis='x', which='major', labelsize=12)
ax.yaxis.tick_left() # where the y axis marks will be

png

Share on

Twitter Facebook LinkedIn

Dimitris Effrosynidis

Skills

Everything seaborn does to create all kinds of plots is here.

Import Libraries

Continious

Categorical

Share on

Leave a Comment

You May Also Enjoy