Python Bullet Charts

[intapiuser]

 However, if we seek to further customize the look of a bullet chart, the R/Python integration comes in very handy! The above image is a bullet chart created using matplotlib, and allows quick interpretation of how well a business is tracking toward its goals.

Inputs:

• df : a dataframe with 4 columns
  • business - the name of the business/category
  • base goal - a numerical value of the quota/base goal
  • stretch goal - a numerical value of the target/stretch goal
  • current value
• color_code: an optional boolean parameter that color codes the current value based on whether the base and stretch goals have been met. Default value set to False

Snippet:

# SQL output is imported as a pandas dataframe variable called "df"
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

# Function: bullet_chart, creates a horizontal bar graph representing a bullet chart
# Inputs: (1) dataframe with 4 rows - the name of the column ("business"), "base goal" or the quota, "stretch goal" or the target, and the "current value" (2) Optional boolean paramater that shades the current value green/yellow/red based on whether the base/stretch goals have been met
# Output: matplotlib image representing a bullet chart
def bullet_chart(df,color_code=False):
  y_pos = np.arange(len(df.index))

  #assign coloring
  df["col"]="indigo"
  if (color_code==True):
    for i in y_pos:
      if(df["current value"][i]>=df["stretch goal"][i]):
        df["col"][i]="green"
      elif(df["current value"][i]>=df["base goal"][i]):
        df["col"][i]="gold"
      else:
        df["col"][i]="lightcoral"

  #Initialize plot
  fig, ax = plt.subplots()
  ax.barh(y_pos, df["stretch goal"], height=0.5, align='center', color='thistle', label="stretch goal")
  ax.barh(y_pos, df["base goal"], height=0.5, align='center', color='mediumorchid', label = "base goal")
  ax.barh(y_pos, df["current value"], height=0.2, align='center',color=df["col"])
  ax.set_yticklabels(df["business"])
  ax.set_yticks(y_pos)
  ax.invert_yaxis()

  #add data labels
  for i in y_pos:
    ax.text(df["current value"][i], i+0.05, df["current value"][i])

  #add legend and format borders
  plt.legend(loc=(0.35,1.0))
  ax.spines['left'].set_linewidth(0.2)
  ax.spines['bottom'].set_linewidth(0)
  ax.spines['right'].set_linewidth(0)
  ax.spines['top'].set_linewidth(0)
  fig.subplots_adjust(left=0.2, top=0.8)

  return fig

# Use Sisense for Cloud Data Teams to visualize a dataframe or an image by passing data to periscope.output()
periscope.image(bullet_chart(df, color_code=True))