I'm starting to learn Pandas and am trying to find the most Pythonic (or panda-thonic?) ways to do certain tasks.

Suppose we have a DataFrame with columns A, B, and C.

- Column A contains boolean values: each row's A value is either true or false.
- Column B has some important values we want to plot.

What we want to discover is the subtle distinctions between B values for rows that have A set to false, vs. B values for rows that have A is true.

In other words, **how can I group by the value of column A (either true or false), then plot the values of column B for both groups on the same graph?** The two datasets should be colored differently to be able to distinguish the points.

Next, let's add another feature to this program: before graphing, we want to compute another value for each row and store it in column D. This value is the mean of all data stored in B for the entire five minutes before a record - but we only include rows that have the same boolean value stored in A.

In other words, **if I have a row where A=True and time=t, I want to compute a value for column D that is the mean of B for all records from time t-5 to t that have the same A=True.**

In this case, how can we execute the groupby on values of A, then apply this computation to each individual group, and finally plot the D values for the two groups?

I think @herrfz hit all the high points. I'll just flesh out the details:

```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
sin = np.sin
cos = np.cos
pi = np.pi
N = 100
x = np.linspace(0, pi, N)
a = sin(x)
b = cos(x)
df = pd.DataFrame({
'A': [True]*N + [False]*N,
'B': np.hstack((a,b))
})
for key, grp in df.groupby(['A']):
plt.plot(grp['B'], label=key)
grp['D'] = pd.rolling_mean(grp['B'], window=5)
plt.plot(grp['D'], label='rolling ({k})'.format(k=key))
plt.legend(loc='best')
plt.show()
```

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