I have 2 tables a 10 by 110 and a 35 by 110 and both contain random numbers from a exponential distribution function provided by my professor. The assignment is to prove the central limit theorem in statistics.

What I thought to try is:

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

"importing data"
df1 = pd.read_excel(r'C:\Users\Henry\Desktop\n10.xlsx')
df2 = pd.read_excel(r'C:\Users\Henry\Desktop\n30.xlsx')
df1avg = pd.read_excel(r'C:\Users\Henry\Desktop\n10avg.xlsx')
df2avg = pd.read_excel(r'C:\Users\Henry\Desktop\n30avg.xlsx')

"plotting n10 histogram"
plt.hist(df1, bins=34)

plt.hist(df1avg, bins=11)


"plotting n30 histogram"
plt.hist(df2, bins=63)

plt.hist(df2avg, bins=11)

Is that ok or do I need to format the tables into a singular column, and if so what is the most efficient way to do that?

CodePudding user response：

I suspect that you will want to flatten your dataframe first, as illustrated below.

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

N = np.random.exponential(1, [40, 5])
df = pd.DataFrame(N) # convert to dataframe
bin_edges = np.linspace(0,6,30)

plt.figure()
plt.hist(df, bins = bin_edges, density = True)
plt.xlabel('Value')
plt.ylabel('Probability density')

The multiple (5) colours of lines per bin shows the histograms for each column of the data frame.

Fortunately, this is not hard to adjust. You can convert the data frame to a numpy array and