利用Python绘制数据的瀑布图的教程

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

index = ['sales','returns','credit fees','rebates','late charges','shipping']
data = {'amount': [350000,-30000,-7500,-25000,95000,-7000]}
trans = pd.DataFrame(data=data,index=index)

from IPython.display import display
display(trans)

display(trans.amount.cumsum())
sales      350000
returns     320000
credit fees   312500
rebates     287500
late charges  382500
shipping    375500
Name: amount, dtype: int64

blank=trans.amount.cumsum().shift(1).fillna(0)
display(blank)
 
sales        0
returns     350000
credit fees   320000
rebates     312500
late charges  287500
shipping    382500
Name: amount, dtype: float64

total = trans.sum().amount
trans.loc["net"] = total
blank.loc["net"] = total
display(trans)
display(blank)

sales        0
returns     350000
credit fees   320000
rebates     312500
late charges  287500
shipping    382500
net       375500
Name: amount, dtype: float64

step = blank.reset_index(drop=True).repeat(3).shift(-1)
step[1::3] = np.nan
display(step)
 
0     0
0    NaN
0  350000
1  350000
1    NaN
1  320000
2  320000
2    NaN
2  312500
3  312500
3    NaN
3  287500
4  287500
4    NaN
4  382500
5  382500
5    NaN
5  375500
6  375500
6    NaN
6    NaN
Name: amount, dtype: float64

blank.loc["net"] = 0

my_plot = trans.plot(kind='bar', stacked=True, bottom=blank,legend=None, title="2014 Sales Waterfall")
my_plot.plot(step.index, step.values,'k')

def money(x, pos):
  'The two args are the value and tick position'
  return "${:,.0f}".format(x)
 
from matplotlib.ticker import FuncFormatter
formatter = FuncFormatter(money)

my_plot = trans.plot(kind='bar', stacked=True, bottom=blank,legend=None, title="2014 Sales Waterfall")
my_plot.plot(step.index, step.values,'k')
my_plot.set_xlabel("Transaction Types")
my_plot.yaxis.set_major_formatter(formatter)

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
 
#Use python 2.7+ syntax to format currency
def money(x, pos):
  'The two args are the value and tick position'
  return "${:,.0f}".format(x)
formatter = FuncFormatter(money)
 
#Data to plot. Do not include a total, it will be calculated
index = ['sales','returns','credit fees','rebates','late charges','shipping']
data = {'amount': [350000,-30000,-7500,-25000,95000,-7000]}
 
#Store data and create a blank series to use for the waterfall
trans = pd.DataFrame(data=data,index=index)
blank = trans.amount.cumsum().shift(1).fillna(0)
 
#Get the net total number for the final element in the waterfall
total = trans.sum().amount
trans.loc["net"]= total
blank.loc["net"] = total
 
#The steps graphically show the levels as well as used for label placement
step = blank.reset_index(drop=True).repeat(3).shift(-1)
step[1::3] = np.nan
 
#When plotting the last element, we want to show the full bar,
#Set the blank to 0
blank.loc["net"] = 0
 
#Plot and label
my_plot = trans.plot(kind='bar', stacked=True, bottom=blank,legend=None, figsize=(10, 5), title="2014 Sales Waterfall")
my_plot.plot(step.index, step.values,'k')
my_plot.set_xlabel("Transaction Types")
 
#Format the axis for dollars
my_plot.yaxis.set_major_formatter(formatter)
 
#Get the y-axis position for the labels
y_height = trans.amount.cumsum().shift(1).fillna(0)
 
#Get an offset so labels don't sit right on top of the bar
max = trans.max()
neg_offset = max / 25
pos_offset = max / 50
plot_offset = int(max / 15)
 
#Start label loop
loop = 0
for index, row in trans.iterrows():
  # For the last item in the list, we don't want to double count
  if row['amount'] == total:
    y = y_height[loop]
  else:
    y = y_height[loop] + row['amount']
  # Determine if we want a neg or pos offset
  if row['amount'] > 0:
    y += pos_offset
  else:
    y -= neg_offset
  my_plot.annotate("{:,.0f}".format(row['amount']),(loop,y),ha="center")
  loop+=1
 
#Scale up the y axis so there is room for the labels
my_plot.set_ylim(0,blank.max()+int(plot_offset))
#Rotate the labels
my_plot.set_xticklabels(trans.index,rotation=0)
my_plot.get_figure().savefig("waterfall.png",dpi=200,bbox_inches='tight')