What is a data visualization design?

This is a summary of data visualization points published in note by Go Ando of THE GUILD, who is famous for services that focus on UX and UI.

https://note.mu/goando/n/n4a2735c3d69d

# 1?

See below for # 1

https://qiita.com/skotaro/items/cdb0732ad1ad2a4b6236

Notes

Regarding the font, it works on mac, but it probably won't work on other OS, so please specify a different font.

plt.rcParams['font.family'] = 'Hiragino Sans'

It is the part of.

8. Do not use the 2nd axis

#Library import
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.ticker import MultipleLocator


#data
mau = [450,480,500,470,520,550,530,580,620,700,690,720]
g_rate = [0,8,0,-4,10,9,-6,12,4,12,-1,8]
g_rate = [one/100 for one in g_rate]
timeindex = pd.date_range('2019/1', periods=12, freq='MS')
mau = pd.Series(mau, index=timeindex, name='MAU(M)')
g_rate = pd.Series(g_rate,index=timeindex,name='Last month growth rate')

#For error avoidance in pandas
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()

#Font settings
plt.rcParams['font.family'] = 'Hiragino Sans'  
plt.rcParams['font.weight'] = 'heavy'

#Split up and down
fig = plt.figure(figsize=(8,5))
ax1 = fig.add_subplot(2, 1, 1)
ax2 = fig.add_subplot(2, 1, 2)

# 1.Change bar color and width
ax1.plot(g_rate.index, g_rate,linewidth=5.,color="saddlebrown")
ax2.bar(mau.index, mau, width=20., color='darkorange', zorder=2, align='center')

# 2.Display y-axis label, specify color and font size
ax1.set_ylabel(g_rate.name,color="saddlebrown",fontsize=14,fontweight='bold')
ax2.set_ylabel(mau.name, color='gray',fontsize=14,fontweight='bold')

# 3.Change y-axis tick position
ax1.yaxis.set_major_locator(MultipleLocator(500))
ax2.yaxis.set_major_locator(MultipleLocator(500))

# 4.Erase x-axis y-axis tick,For the graph above, erase all x-axis
ax1.tick_params(labelbottom="off",bottom=False, left=False) 
ax1.set_xticklabels([]) 
ax2.tick_params(bottom=False, left=False)

# 5.Change the color of the x-axis and y-axis tick labels
ax1.tick_params(axis='y', colors='dimgray')
ax2.tick_params(axis='x', colors='dimgray')
ax2.tick_params(axis='y', colors='dimgray')

# 6.Show y-axis grid, color black for graph above
ax1.grid(axis='y',color="black")
ax2.grid(axis='y')

# 7.Erase the right frame
ax1.spines['right'].set_visible(False)
ax2.spines['right'].set_visible(False)

# 8.Change the color of the frame
ax1.spines['bottom'].set_color('dimgray')
ax1.spines['top'].set_color('dimgray')
ax1.spines['left'].set_color('dimgray')
ax2.spines['bottom'].set_color('dimgray')
ax2.spines['top'].set_color('dimgray')
ax2.spines['left'].set_color('dimgray')

# 9.Set x-axis tick label to month only
# '%-m'Is strftime leading zero no month notation
ax2.xaxis.set_major_formatter(mdates.DateFormatter('%-m')) 

# 10.Set the x-axis tick position every month
ax2.xaxis.set_major_locator(mdates.MonthLocator())
ax2.xaxis.set_tick_params(labelsize=12)

#The major tick position is not updated unless the fig is drawn once
fig.canvas.draw()

# 11.Specifying the range of the vertical axis
ax1.set_ylim(-0.20,0.20)
ax2.set_ylim(0,800)
    
# 12.Change of vertical axis tick
vals_1 = [-0.2,0,0.2]
ax1.set_yticks(vals_1)
ax1.set_yticklabels(['{:,.0%}'.format(x) for x in vals_1],color="saddlebrown",fontsize=12,fontweight='bold')
vals_2 = [0,400,800]
ax2.set_yticks(vals_2)
ax2.set_yticklabels(['{}'.format(x) for x in vals_2] ,fontsize=12,fontweight='bold')

# 13.Align ylabel
axs = [ax1,ax2]
fig.align_ylabels(axs)

For how to set the vertical axis to% in # 12., I referred to the answer from stackoverflow [^ 1].

13. is based on the official doc of matplotlib [^ 2].

You can also write the y label vertically by starting a new line character by character, but I didn't because the code was ugly. (Please tell me if there is a smart way)

9. Reduce elements to a minimum

#data
shipment_n = pd.DataFrame([52217, 9113, 4078],
                           index=['iPhone', 'iPad', 'Mac'],
                           columns=['amount'])

fig, ax = plt.subplots(figsize=(6, 3))
ax.bar(shipment_n.index,shipment_n["amount"])

# 1.Erase the frame other than bottom
sides = ['left','right', 'top']
[ax.spines[side].set_visible(False) for side in sides] 

# 2.Change the color of the bottom frame to gray
ax.spines['bottom'].set_color("dimgray")

# 3.Erase the y-axis tick
ax.tick_params(left=False, labelleft=False)

# 4.Larger x-axis label size, gray color
ax.tick_params(axis='x', labelsize='xx-large',color="dimgray",labelcolor="dimgray")

# 5.Show actual value above the bar
vmax = shipment_n['amount'].max()
for i, value in enumerate(shipment_n['amount']):
    ax.text(i, value+vmax*0.06, f'{value:,}', fontsize='xx-large', va='center', ha='center', color='C0')

4 is achieved by offsetting using vmax.

10. Write the largest key message

#color
ori_blue = "#3674b3"
ori_lightblue = "#b3daff"


# 1.Paint the whole
plt.rcParams["axes.facecolor"] = (1,1,1,0)

# 2.Blue background
fig, ax = plt.subplots(figsize=(6, 4),facecolor=ori_blue)

# 3.title
fig.suptitle("iPhone accounts for 80% of shipments",color="white",size=24,fontweight='bold',ha='center')

# 4.Plot in light blue
ax.bar(shipment_n.index,shipment_n["amount"],color=ori_lightblue)

# 5.Add a description
ax.text(-0.4,shipment_n["amount"].max()*1.2,"Chart: 2017 Apple Shipment Devices",color=ori_lightblue,fontsize=12)

#6.Delete frames other than bottom
sides = ['left','right', 'top']
[ax.spines[side].set_visible(False) for side in sides] 

# 7.bottom frame color setting
ax.spines['bottom'].set_color(ori_lightblue)

# 8.y-axis range setting
ax.set_ylim(0,80000)

# 9.Erase the y-axis tick label
ax.tick_params(left=False, labelleft=False)
    
# 10.x-axis label setting
ax.tick_params(axis='x', labelsize='xx-large',color=ori_lightblue,labelcolor=ori_lightblue)

# 11.Show actual value above the bar
vmax = shipment_n['amount'].max()
for i, value in enumerate(shipment_n['amount']):
    ax.text( i,value+vmax*0.06, f'{value:,}', fontsize='xx-large', va='center', ha='center', color=ori_lightblue)

#When saving
plt.savefig("savefig_with_facecolor.png ",facecolor=fig.get_facecolor())

About the color, I set it somehow. In the head family, the corners of the background were rounded, but this could not be reproduced. (I did not come out even after checking it quite a bit ...) Also, when saving with plt.savefig (), it is necessary to specify the background color with facecolor = [^ 3].

Implement "Data Visualization Design # 2" with matplotlib