Create a line graph per bin in Python 3 - python-3.x

Question

I have a dataframe called 'games':
Game_id Goals P_value
1 2 0.4
2 3 0.321
45 0 0.64
I need to split the P value to 0.05 steps, bin the rows per P value and than create a line graph that shows the sum per p value.
What I currently have:
games.set_index('p value', inplace=True)
games.sort_index()
np.cumsum(games['goals']).plot()
But I get this:
No matter what I tried, I couldn't group the P values and show the sum of goals per P value..
I also tried to use matplotlib.pyplot but than I couldn't use the cumsum function..

Answer 1

If I understood you correctly, you want to have discrete steps in the p-value of width 0.05 and show the cumulative sum?
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# create some random example data
df = pd.DataFrame({
'goals': np.random.poisson(3, size=1000),
'p_value': np.random.uniform(0, 1, size=1000)
})
# define binning in p-value
bin_edges = np.arange(0, 1.025, 0.05)
bin_center = 0.5 * (bin_edges[:-1] + bin_edges[1:])
bin_width = np.diff(bin_edges)
# find the p_value bin, each row belongs to
# 0 is underflow, len(edges) is overflow bin
df['bin'] = np.digitize(df['p_value'], bins=bin_edges)
# get the number of goals per p_value bin
goals_per_bin = df.groupby('bin')['goals'].sum()
print(goals_per_bin)
# not every bin might be filled, so we will use pandas index
# matching t
binned = pd.DataFrame({
'center': bin_center,
'width': bin_width,
'goals': np.zeros(len(bin_center))
}, index=np.arange(1, len(bin_edges)))
binned['goals'] = goals_per_bin
plt.step(
binned['center'],
binned['goals'],
where='mid',
)
plt.xlabel('p-value')
plt.ylabel('goals')
plt.show()