Data Visualization I

Preparing Datasets

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


# Make a data frame
df=pd.DataFrame({'x': range(1,11), 'y1': np.random.randn(10), 'y2': np.random.randn(10)+range(1,11), 'y3': np.random.randn(10)+range(11,21), 'y4': np.random.randn(10)+range(6,16), 'y5': np.random.randn(10)+range(4,14)+(0,0,0,0,0,0,0,-3,-8,-6), 'y6': np.random.randn(10)+range(2,12), 'y7': np.random.randn(10)+range(5,15), 'y8': np.random.randn(10)+range(4,14), 'y9': np.random.randn(10)+range(4,14), 'y10': np.random.randn(10)+range(2,12) })

df['x']=pd.Categorical(df['x'])
print(df.dtypes)
df.head(10)

x      category
y1      float64
y2      float64
y3      float64
y4      float64
y5      float64
y6      float64
y7      float64
y8      float64
y9      float64
y10     float64
dtype: object

	x	y1	y2	y3	y4	y5	y6	y7	y8	y9	y10
0	1	0.953949	2.878138	8.866186	7.169483	4.894806	1.383657	5.207700	2.749603	3.777047	4.338875
1	2	0.148130	2.182593	11.399598	5.671996	5.013517	2.503236	5.270997	3.332785	4.707431	3.470963
2	3	0.401601	3.342006	12.787722	7.898548	6.005761	5.079653	7.948136	7.321007	5.849269	3.589841
3	4	1.325920	4.496024	15.672599	9.664751	6.800054	3.797002	8.461314	7.364939	7.597704	4.230760
4	5	0.045468	4.553081	14.898799	10.109224	6.771789	6.317487	9.446912	9.109398	7.054921	5.494813
5	6	0.385796	6.015589	15.090889	10.442972	9.406430	5.064227	10.006732	6.563899	12.320422	7.353816
6	7	0.316242	5.671259	17.911464	11.287174	9.375809	7.951598	9.014010	11.649927	11.138327	7.948010
7	8	1.639019	7.694177	19.323432	12.289224	7.487505	8.626360	12.696997	11.324753	9.214215	8.570674
8	9	-0.886327	10.187601	18.373330	14.499094	4.679189	10.825828	13.044199	10.468501	11.931833	9.631274
9	10	-0.261461	8.573481	19.369957	13.711514	7.270583	11.427754	13.084936	13.267057	13.893086	11.912090

Matlibplot

Resolution

• We can increase the dpi of the matplotlib parameters to get image of higher resolution in notebook

• The dpi setting has to GO before the magic inline command because the magic inline commened resets the dpi to default

## Change DPI for higher resolution in notebook
%matplotlib inline

matplotlib.rcParams['figure.dpi'] = 150
matplotlib.rcParams['savefig.dpi'] = 150
# Change DPI when saving graphs in files
# matplotlib.rc("savefig", dpi=dpi)

Matplotlib Style

# available style
print(plt.style.available)

['Solarize_Light2', '_classic_test_patch', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'tableau-colorblind10']

# choose one style
plt.style.use('fivethirtyeight')

Matplotlib Chinese Issues

## Setting Chinese Fonts
## Permanent Setting Version
plt.rcParams['font.sans-serif']=["PingFang HK"]
plt.rcParams['axes.unicode_minus']= False

Plotting

## Simple X and Y
plt.plot(df['x'], df['y1'])
plt.show()

## Simple X and two Y's
plt.plot(df['x'], df['y1'])
plt.plot(df['x'], df['y2'])
plt.show()

## Adding legends
## Simple X and two Y's
plt.plot(df['x'], df['y1'], label="資料1")
plt.plot(df['x'], df['y2'], label="資料2")
plt.legend()
plt.tight_layout()
plt.show()

## Save graphs
## Simple X and two Y's
plt.plot(df['x'], df['y1'], label="資料1")
plt.plot(df['x'], df['y2'], label="資料2")
plt.legend()
plt.tight_layout()
plt.savefig('plot.png')
plt.show()

## Add x/y labels and title
## Simple X and two Y's
plt.plot(df['x'], df['y1'], label="資料1")
plt.plot(df['x'], df['y2'], label="資料2")
plt.legend()
plt.xlabel("X軸")
plt.ylabel("Y軸")
plt.title("漂亮的圖")
plt.tight_layout()
plt.show()

Bar Plots

## Normal bar plot
plt.bar(df['x'], df['y3'])
plt.show()

## Sort bars according to values

df_sorted = df.sort_values(['y3','y2'], ascending=True)
print(df_sorted.dtypes)
df_sorted

x      category
y1      float64
y2      float64
y3      float64
y4      float64
y5      float64
y6      float64
y7      float64
y8      float64
y9      float64
y10     float64
dtype: object

	x	y1	y2	y3	y4	y5	y6	y7	y8	y9	y10
0	1	0.953949	2.878138	8.866186	7.169483	4.894806	1.383657	5.207700	2.749603	3.777047	4.338875
1	2	0.148130	2.182593	11.399598	5.671996	5.013517	2.503236	5.270997	3.332785	4.707431	3.470963
2	3	0.401601	3.342006	12.787722	7.898548	6.005761	5.079653	7.948136	7.321007	5.849269	3.589841
4	5	0.045468	4.553081	14.898799	10.109224	6.771789	6.317487	9.446912	9.109398	7.054921	5.494813
5	6	0.385796	6.015589	15.090889	10.442972	9.406430	5.064227	10.006732	6.563899	12.320422	7.353816
3	4	1.325920	4.496024	15.672599	9.664751	6.800054	3.797002	8.461314	7.364939	7.597704	4.230760
6	7	0.316242	5.671259	17.911464	11.287174	9.375809	7.951598	9.014010	11.649927	11.138327	7.948010
8	9	-0.886327	10.187601	18.373330	14.499094	4.679189	10.825828	13.044199	10.468501	11.931833	9.631274
7	8	1.639019	7.694177	19.323432	12.289224	7.487505	8.626360	12.696997	11.324753	9.214215	8.570674
9	10	-0.261461	8.573481	19.369957	13.711514	7.270583	11.427754	13.084936	13.267057	13.893086	11.912090

plt.bar('x', 'y3', data=df_sorted)
plt.show()

## Horizontal Bars
plt.bar('x', 'y4', data=df.sort_values('y4'))
plt.tight_layout()
plt.show()

Pie Chart

plt.style.use("fivethirtyeight")

slices = [20, 30, 30, 20]
labels = ['Attendance', 'Midterm', 'Final', 'Assignments']
explode = [0, 0, 0.1, 0]

plt.pie(slices, labels=labels, explode=explode, shadow=True,
        startangle=90, autopct='%1.1f%%',
        wedgeprops={'edgecolor': 'black'})

plt.title("Grading Policy")
plt.tight_layout()
plt.show()

Stacked Plot

plt.style.use("fivethirtyeight")


minutes = [1, 2, 3, 4, 5, 6, 7, 8, 9]

player1 = [1, 2, 3, 3, 4, 4, 4, 4, 5]
player2 = [1, 1, 1, 1, 2, 2, 2, 3, 4]
player3 = [1, 1, 1, 2, 2, 2, 3, 3, 3]

labels = ['player1', 'player2', 'player3']
colors = ['#6d904f', '#fc4f30', '#008fd5']

plt.stackplot(minutes, player1, player2, player3, labels=labels, colors=colors)

plt.legend(loc='upper left')

plt.title("Stacked Plot")
plt.tight_layout()
plt.show()

Histogram

import random
import numpy as np

# grades = [random.randint(0,100) for i in range(1000)]

grades = np.random.normal(85, 13, 10000)
#bins = [50, 60, 70, 80, 90,100]

plt.hist(grades, bins= 50,edgecolor='black')

reference_line = np.mean(grades)
color = '#fc4f30'

plt.axvline(reference_line, color=color, label='Mean Score', linewidth=2)

plt.legend()

plt.title('Histogram')
plt.xlabel('Student Grades')
plt.ylabel('個數')

plt.tight_layout()

plt.show()

Scatter Plot

plt.figure(figsize=(7,5), dpi=300)
plt.scatter(df['y4'], df['y5'], c=df['y6'], s=df['y6']*100,cmap='summer',
            edgecolor='black', linewidth=1, alpha=0.75)

cbar = plt.colorbar()
cbar.set_label('Y6數值大小')

# plt.xscale('log')
# plt.yscale('log')

plt.title('3D Scatterplot')
plt.xlabel('Y1')
plt.ylabel('Y2')

plt.ylim((0,13))

plt.show()

Complex Graphs

df

	x	y1	y2	y3	y4	y5	y6	y7	y8	y9	y10
0	1	0.953949	2.878138	8.866186	7.169483	4.894806	1.383657	5.207700	2.749603	3.777047	4.338875
1	2	0.148130	2.182593	11.399598	5.671996	5.013517	2.503236	5.270997	3.332785	4.707431	3.470963
2	3	0.401601	3.342006	12.787722	7.898548	6.005761	5.079653	7.948136	7.321007	5.849269	3.589841
3	4	1.325920	4.496024	15.672599	9.664751	6.800054	3.797002	8.461314	7.364939	7.597704	4.230760
4	5	0.045468	4.553081	14.898799	10.109224	6.771789	6.317487	9.446912	9.109398	7.054921	5.494813
5	6	0.385796	6.015589	15.090889	10.442972	9.406430	5.064227	10.006732	6.563899	12.320422	7.353816
6	7	0.316242	5.671259	17.911464	11.287174	9.375809	7.951598	9.014010	11.649927	11.138327	7.948010
7	8	1.639019	7.694177	19.323432	12.289224	7.487505	8.626360	12.696997	11.324753	9.214215	8.570674
8	9	-0.886327	10.187601	18.373330	14.499094	4.679189	10.825828	13.044199	10.468501	11.931833	9.631274
9	10	-0.261461	8.573481	19.369957	13.711514	7.270583	11.427754	13.084936	13.267057	13.893086	11.912090

# create a color palette
palette = plt.get_cmap('Set1')
 
# multiple line plot
num=0
#plt.figure(figsize=(5,3), dpi=150)
for column in df.drop(['x','y10'], axis=1):
    num+=1
    plt.plot(df['x'], df[column], marker='', color=palette(num), linewidth=1, alpha=0.9, label=column)
 
plt.legend(loc=2, ncol=2)
 
# Add titles
plt.title("Line Plot With Several Values", loc='left', fontsize=12, fontweight=0, color='orange')
plt.xlabel("Time")
plt.ylabel("Score")

Text(0, 0.5, 'Score')

import matplotlib.pyplot as plt
plt.style.use('ggplot')

df_columns = df.drop(['x','y1'], axis=1).columns
palette = plt.get_cmap('tab20')

num=0

fig, ax = plt.subplots(nrows=3, ncols=3, sharex=True, sharey=True, figsize=(8, 6), dpi=100)

for row in range(3):
    for col in range(3):
        ax[row,col].plot(df['x'],df[df_columns[num]],color=palette(num), linewidth=1.9, alpha=0.9, label=df_columns[num])
        ax[row,col].set_title(df_columns[num],loc='left', fontsize=14,color=palette(num))
        num+=1
fig.suptitle("Facet Grids", fontsize=14, fontweight=0, color='black', style='italic', y=1.02)
fig.text(0.5, 0.0, 'Common X', ha='center', fontsize=14)
fig.text(0.0, 0.5, 'Common Y', va='center', rotation='vertical', fontsize=14)

Text(0.0, 0.5, 'Common Y')

Seaborn Module

Two Types of Functions

• Figure-level functions (Generic)

• Axex-level functions (Specific)

## Change the DPI

import seaborn as sns
sns.set(rc={"figure.dpi":300, 'savefig.dpi':300})
sns.set_context('notebook')
sns.set_style("ticks")

sns.set(style='darkgrid')

penguins = sns.load_dataset('penguins')
penguins.head()

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	Male
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	Female
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	Female
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	Female

# histogram
print(sns.__version__) # seaborn>=0.11.0
sns.displot(data=penguins, x="flipper_length_mm", hue="species", multiple="stack")

0.11.0

<seaborn.axisgrid.FacetGrid at 0x7fb050c132e8>

sns.displot(data=penguins, x="flipper_length_mm", hue="species", multiple="stack")

<seaborn.axisgrid.FacetGrid at 0x7fb040877860>

sns.displot(data=penguins, x="flipper_length_mm", hue="species", col="species")

<seaborn.axisgrid.FacetGrid at 0x7fb050bf5eb8>

## kernel density plot
sns.kdeplot(data=penguins, x='flipper_length_mm', hue='species', multiple="stack")

<AxesSubplot:xlabel='flipper_length_mm', ylabel='Density'>

sns.displot(data=penguins, x="flipper_length_mm", hue="species", multiple="stack", kind="kde")

<seaborn.axisgrid.FacetGrid at 0x7fb050a358d0>

tips = sns.load_dataset("tips")

g = sns.relplot(data=tips, x="total_bill", y="tip")
g.ax.axline(xy1=(10,2), slope=.2, color="b", dashes=(5,2))

<matplotlib.lines._AxLine at 0x7fb032fe64e0>

g = sns.relplot(data=penguins, x="flipper_length_mm", y="bill_length_mm", col="sex")
g.set_axis_labels("Flipper length (mm)", "Bill length (mm)")

<seaborn.axisgrid.FacetGrid at 0x7fb0209e5c50>

sns.catplot(data=penguins, x='species', y='flipper_length_mm', kind="box")

<seaborn.axisgrid.FacetGrid at 0x7fb032f99198>

• jointplot(): plots the relationship or joint distribution of two variables while adding marginal axes that show the univariate distribution of each one separately

sns.jointplot(data=penguins, x="flipper_length_mm", y="bill_length_mm", hue="species")

<seaborn.axisgrid.JointGrid at 0x7fb020a01710>

• pairplot(): visualizes every pairwise combination of variables simultaneously in a data frame

sns.pairplot(data=penguins, hue="species")

<seaborn.axisgrid.PairGrid at 0x7fb0408cb2b0>

Long-format vs. Wide-format Data

flights = sns.load_dataset("flights")
flights.head()

	year	month	passengers
0	1949	Jan	112
1	1949	Feb	118
2	1949	Mar	132
3	1949	Apr	129
4	1949	May	121

sns.relplot(data=flights, x="year", y="passengers", hue="month", kind="line")

<seaborn.axisgrid.FacetGrid at 0x7fb032dd7630>

sns.relplot(data=flights, x="month", y="passengers", hue="year", kind="line")

<seaborn.axisgrid.FacetGrid at 0x7fb040fe3438>

flights_wide = flights.pivot(index="year", columns="month", values="passengers")
flights_wide.head()

month	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
year
1949	112	118	132	129	121	135	148	148	136	119	104	118
1950	115	126	141	135	125	149	170	170	158	133	114	140
1951	145	150	178	163	172	178	199	199	184	162	146	166
1952	171	180	193	181	183	218	230	242	209	191	172	194
1953	196	196	236	235	229	243	264	272	237	211	180	201

print(type(flights_wide))

<class 'pandas.core.frame.DataFrame'>

sns.catplot(data=flights_wide, kind="box")

<seaborn.axisgrid.FacetGrid at 0x7fb040fc6be0>

Chinese Fonts Issues

• Find system-compatible Chinese fonts using the terminal command:

!fc-list :lang=zh

• Define the font to be used as well as the font properties in Python:

from matplotlib import rcParams
from matplotlib.font_manager import FontProperties
import matplotlib.pyplot as plt
# rcParams['axes.unicode_minus']=False
myfont = FontProperties(fname='/Library/Fonts/Songti.ttc',
 size=15)
plt.title('圖表標題', fontproperties=myfont)
plt.ylabel('Y軸標題', fontproperties=myfont)
plt.legend(('分類一', '分類二', '分類三'), loc='best', prop=myfont)

• For a permanent solution, please read references.

  • Modify the setting file in matplotlib: matplotlib.matplotlib_fname() to get the file path

  • It’s similar to: /Users/YOUR_NAME/opt/anaconda3/lib/python3.7/site-packages/matplotlib/mpl-data/matplotlibrc

  • Two important parameters: font.family and font.serif

  • Add the font name under font.serif. My case: Source Han Sans

## One can set the font preference permanently
## in the setting file
import matplotlib
matplotlib.matplotlib_fname()

'/Users/Alvin/opt/anaconda3/envs/python-notes/lib/python3.7/site-packages/matplotlib/mpl-data/matplotlibrc'

from matplotlib import rcParams
from matplotlib.font_manager import FontProperties
import matplotlib.pyplot as plt
# rcParams['axes.unicode_minus']=False
#/Users/alvinchen/Library/Fonts/SourceHanSans.ttc
#'/System/Library/Fonts/PingFang.ttc'
def getChineseFont(size=15):  
    return FontProperties(fname='/Users/Alvin/Library/Fonts/SourceHanSans.ttc',size=size)  

print(getChineseFont().get_name())
plt.title('圖表標題', fontproperties=getChineseFont(20))
plt.ylabel('Y軸標題', fontproperties=getChineseFont(12))
plt.legend(('分類一', '分類二', '分類三'), loc='best', prop=getChineseFont())

Source Han Sans

<matplotlib.legend.Legend at 0x7fb033c97588>

## Permanent Setting Version
plt.rcParams['font.sans-serif']=["PingFang HK"]
plt.rcParams['axes.unicode_minus']= False

plt.plot((2,4,6), (3,5,7))
plt.title("中文標題")
plt.ylabel("y軸標題")
plt.xlabel("x軸標題")
plt.show()

## Seaborn
sns.set(font=['san-serif'])
sns.set_style("whitegrid",{"font.sans-serif":["PingFang HK"]})
cities_counter = [('好棒', 285), ('給我', 225), ('不要', 163), ('細柔', 136), ('吃飯', 130), ('小小', 124), ('深圳', 88), ('溫州', 67), ('小知', 66), ('大之', 45)]
sns.set_color_codes("pastel")
sns.barplot(x=[k for k, _ in cities_counter[:10]], y=[v for _, v in cities_counter[:10]])

<AxesSubplot:>

References

• Python Graph Gallery

• Corey Schafer’s YouTube matplotlib tutorial

• Matplotlib colormaps

• 解決Python 3 Matplotlib與Seaborn視覺化套件中文顯示問題

• Day 11 : Google Colab 實用奧步篇 ( 連結硬碟、繪圖中文顯示問題 )

• Souce Han Sans (open-source)

• Seaborn Documentation

Requirements

seaborn==0.11.0
pandas==1.1.2
numpy==1.18.1
matplotlib==3.3.2