try.py

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

empty = []
cols = ["100", "500", "1000", "5000", "7000", "10000", "15000"]
arr = [int(x) for x in cols]
for i in range(len(arr)):
    pin = 0
    ptot = 0
    sample = []
    for j in range(arr[i]):
        x = random.uniform(-1, 1)
        y = random.uniform(-1, 1)
        if (x**2 + y**2) <= 1:
            pin += 1
        ptot += 1
        pi = 4 * (pin / ptot)
        sample.append(pi)
    empty.append(sample)
column = [
    "min",
    "max",
    "mean",
    "median",
    "stand-dev",
    "variance",
    "skewness",
    "kurtosis",
]
stats_data = pd.DataFrame(data=np.zeros((7, 8)), columns=column, index=arr)
min, max, mean, median, stdev, var, skew, kurt = [], [], [], [], [], [], [], []
for i in range(len(arr)):
    raw = pd.DataFrame(
        data=empty[i], columns={cols[i]}, index=np.arange(1, len(empty[i]) + 1)
    )
    min.append(raw[str(cols[i])].min())
    max.append(raw[str(cols[i])].max())
    mean.append(raw[str(cols[i])].mean())
    median.append(raw[str(cols[i])].median())
    stdev.append(raw[str(cols[i])].std())
    var.append(raw[str(cols[i])].var())
    skew.append(raw[str(cols[i])].skew())
    kurt.append(raw[str(cols[i])].kurtosis())
    # sns.displot(data=raw, x=str(cols[i]), kind="kde")
    # raw.to_csv(str(cols[i]) + ".csv")
    del raw
stats_data["min"] = min
stats_data["max"] = max
stats_data["mean"] = mean
stats_data["median"] = median
stats_data["stand-dev"] = stdev
stats_data["variance"] = var
stats_data["skewness"] = skew
stats_data["kurtosis"] = kurt
print(stats_data)
# stats_data.to_csv("stats.csv")
plt.show()

test = empty[3]
print(len(test))