python 판다스(pandas) DataFrame matplotlib plotting 그래프 만들기

DataFrame Plotting

 

matplotlib 불러오기, matplotlib 스타일 선택

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

 

참고 : https://matplotlib.org/stable/plot_types/index.html

Plot types — Matplotlib 3.8.1 documentation

sample data 생성

a = np.random.normal(0,1,999)
b = np.random.normal(1,2,999)
c = np.random.normal(2,3,999)
df = pd.DataFrame([a,b,c]).T
df.columns =['A', 'B', 'C']

선 도표

df1 = df.cumsum()
ax = df1.plot()

ax.set_title('Title')
ax.set_xlabel('X Axis')
ax.set_ylabel('Y Axis')

fig = ax.figure
fig.set_size_inches(8, 3)
fig.tight_layout(pad=1)
fig.savefig('filename.png', dpi=125)

plt.close()

박스 플롯

ax = df.plot.box(vert=False)
ax = df.plot.box(column='c1', by='c2')

히스토그램

ax = df['A'].plot.hist(bins=20)

여러 히스토그램(중복 또는 누적)

ax = df.plot.hist(bins=25, alpha=0.5) # or
ax = df.plot.hist(bins=25, stacked=True)

막대 그래프

bins = np.linspace(-10,15,26)
binned = pd.DataFrame()
for x in df.columns:
	 y=pd.cut(df[x],bins,labels=bins[:-1])
	 y=y.value_counts().sort_index()
 binned = pd.concat([binned,y],axis=1)
binned.index = binned.index.astype(float)
binned.index += (np.diff(bins) / 2.0)
ax = binned.plot.bar(stacked=False,
	 width=0.8) # for bar width

수평 막대

ax = binned['A'][(binned.index >= -4) &
 	(binned.index <= 4)].plot.barh()

밀도 플롯(Density plot)

ax = df.plot.kde()

산포도(Scatter plot)

ax = df.plot.scatter(x='A', y='C')

파이 차트

s = pd.Series(data=[10, 20, 30],
 index = ['dogs', 'cats', 'birds'])
ax = s.plot.pie(autopct='%.1f')

ax.set_title('Pie Chart')
ax.set_aspect(1) 
ax.set_ylabel('') # remove default

fig = ax.figure
fig.set_size_inches(8, 3)
fig.savefig('filename.png', dpi=125)

plt.close(fig)

표시된 범위(range) 변경

ax.set_xlim([-5, 5])
# for some white space on the chart 

lower, upper = ax.get_ylim()
ax.set_ylim([lower-1, upper+1])

차트에 각주(footnote) 추가

# after the fig.tight_layout(pad=1) above

fig.text(0.99, 0.01, 'Footnote',
     ha='right', va='bottom',
     fontsize='x-small',
     fontstyle='italic', color='#999999')

같은 차트에 있는 선과 막대

# start with sample data 
s = pd.Series(np.random.normal(
	 1.02, 0.015, 40))
s = s.cumprod()
dfg = (pd.concat([s / s.shift(1),
 	s / s.shift(4)], axis=1) * 100) - 100
dfg.columns = ['Quarter', 'Annual']
dfg.index = pd.period_range('2010-Q1',
 	periods=len(dfg), freq='Q')
 
# reindex with integers from 0; keep old
old = dfg.index
dfg.index = range(len(dfg))

# plot the line from pandas
ax = dfg['Annual'].plot(color='blue',
	 label='Year/Year Growth')
 
# plot the bars from pandas
dfg['Quarter'].plot.bar(ax=ax,
 	label='Q/Q Growth', width=0.8)
 
# relabel the x-axis more appropriately
ticks = dfg.index[((dfg.index+0)%4)==0]
labs = pd.Series(old[ticks]).astype(str)
ax.set_xticks(ticks)
ax.set_xticklabels(labs.str.replace('Q',
 	'\nQ'), rotation=0)
 
# fix the range of the x-axis … skip 1st
ax.set_xlim([0.5,len(dfg)-0.5])

# add the legend
l=ax.legend(loc='best',fontsize='small')

ax.set_title('Fake Growth Data')
ax.set_xlabel('Quarter')
ax.set_ylabel('Per cent')
fig = ax.figure
fig.set_size_inches(8, 3)
fig.tight_layout(pad=1)
fig.savefig('filename.png', dpi=125)

plt.close()

 

 

참고 : https://www.geeksforgeeks.org/pandas-cheat-sheet

Pandas Cheat Sheet for Data Science in Python