Pandas Matplotlib Line Graph - python-3.x

Question

Given the following data frame:
import pandas as pd
import numpy as np
df = pd.DataFrame(
{'YYYYMM':[201603,201503,201403,201303,201603,201503,201403,201303],
'Count':[5,6,2,7,4,7,8,9],
'Group':['A','A','A','A','B','B','B','B']})
df
Count Group YYYYMM
0 5 A 201603
1 6 A 201503
2 2 A 201403
3 7 A 201303
4 4 B 201603
5 7 B 201503
6 8 B 201403
7 9 B 201303
I need to generate a line graph with one line per group with a summary table at the bottom. Something like this:
I need each instance of 'YYYYMM' to be treated like a year by Pandas/Matplotlib.
So far, this seems to help, but I'm not sure if it will do the trick:
df['YYYYMM']=df['YYYYMM'].astype(str).str[:-2].astype(np.int64)
Then, I did this to pivot the data:
t=df.pivot_table(df,index=['YYYYMM'],columns=['Group'],aggfunc=np.sum)
Count
Group A B
YYYYMM
2013 7 9
2014 2 8
2015 6 7
2016 5 4
Then, I tried to plot it:
import matplotlib.pyplot as plt
%matplotlib inline
fig, ax = plt.subplots(1,1)
t.plot(table=t,ax=ax)
...and this happened:
I'd like to do the following:
remove all lines (borders) from the table at the bottom
remove the jumbled text in the table
remove the x axis tick labels (it should just show the years for tick labels)
I can clean up the rest myself (remove legend and borders, etc..).
Thanks in advance!

Answer 1

I may not have fully understood what you mean by 1., since you are showing the table lines in your reference. I have also not understood whether you want to transpose the table.
What you may be looking for is:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.DataFrame(
{'YYYYMM':[201603,201503,201403,201303,201603,201503,201403,201303],
'Count':[5,6,2,7,4,7,8,9],
'Group':['A','A','A','A','B','B','B','B']})
df['YYYYMM']=df['YYYYMM'].astype(str).str[:-2].astype(int)
t=pd.pivot_table(df, values='Count', index='YYYYMM',columns='Group',aggfunc=np.sum)
t.index.name = None
fig, ax = plt.subplots(1,1)
t.plot(table=t,ax=ax)
ax.xaxis.set_major_formatter(plt.NullFormatter())
plt.tick_params(
axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
labelbottom='off') # labels along the bottom edge are off
plt.show()