Here is my code so far:
import numpy as np
import matplotlib.pyplot as plt

#data = (3,6, 9, 12, 15)
#fig, simple_chart = plt.subplots()
#simple_chart.plot(data)
#plt.show()

#population 1
data1 = np.loadtxt('data1.txt')
#mean
population1mean = np.mean(data1)
print(population1mean)
#standard deviation
population1SD = np.std(data1)
print(population1SD)
#histogram
plt.hist(data1, bins='auto')
plt.title('Population 1 Histogram')
plt.xlabel('x')
plt.ylabel('Frequency')
plt.show()
# Generate random samples for different combinations of n and k
pairs1 = [
    np.random.choice(data1, size=(2, 10)),
    np.random.choice(data1, size=(2, 100)),
    np.random.choice(data1, size=(2, 1000)),
    np.random.choice(data1, size=(20, 100)),
    np.random.choice(data1, size=(20, 1000)),
    np.random.choice(data1, size=(200, 100)),
    np.random.choice(data1, size=(200, 1000))
]

# Calculate means and standard deviations for each pair of n and k
means = [np.mean(pair) for pair in pairs1]
stds = [np.std(pair) for pair in pairs1]

# Plot histograms for means and standard deviations
for i, pair in enumerate(pairs1):
    fig, axs = plt.subplots(1, 2, figsize=(10, 5))

    axs[0].hist(pair.mean(axis=0), bins='auto',edgecolor='black')
    axs[0].set_title(f"n={pair.shape[0]}, k={pair.shape[1]} Mean")
    axs[0].set_xlabel('Mean')
    axs[0].set_ylabel('Frequency')

    axs[1].hist(pair.std(axis=0), bins='auto',edgecolor='black')
    axs[1].set_title(f"n={pair.shape[0]}, k={pair.shape[1]} Std")
    axs[1].set_xlabel('Standard Deviation')
    axs[1].set_ylabel('Frequency')

    plt.tight_layout()
    plt.show()


#population 2
data2 = np.loadtxt('data2.txt')
#mean
population2mean = np.mean(data2)
print(population2mean)
#standard deviation
population2SD = np.std(data2)
print(population2SD)
#histogram
plt.hist(data2, bins='auto')
plt.title('Population 2 Histogram')
plt.xlabel('x')
plt.ylabel('Frequency')
plt.show()

# Generate random samples for different combinations of n and k
pairs2 = [
    np.random.choice(data2, size=(2, 10)),
    np.random.choice(data2, size=(2, 100)),
    np.random.choice(data2, size=(2, 1000)),
    np.random.choice(data2, size=(20, 100)),
    np.random.choice(data2, size=(20, 1000)),
    np.random.choice(data2, size=(200, 100)),
    np.random.choice(data2, size=(200, 1000))
]

# Calculate means and standard deviations for each pair of n and k
means = [np.mean(pair) for pair in pairs2]
stds = [np.std(pair) for pair in pairs2]

# Plot histograms for means and standard deviations
for i, pair in enumerate(pairs2):
    fig, axs = plt.subplots(1, 2, figsize=(10, 5))

    axs[0].hist(pair.mean(axis=0), bins='auto',edgecolor='black')
    axs[0].set_title(f"n={pair.shape[0]}, k={pair.shape[1]} Mean")
    axs[0].set_xlabel('Mean')
    axs[0].set_ylabel('Frequency')

    axs[1].hist(pair.std(axis=0), bins='auto',edgecolor='black')
    axs[1].set_title(f"n={pair.shape[0]}, k={pair.shape[1]} Std")
    axs[1].set_xlabel('Standard Deviation')
    axs[1].set_ylabel('Frequency')

    plt.tight_layout()
    plt.show()

For each pair of n and k in population 1 and population 2, please find the following:
E(x) = expected value of sample means
S(x) = standard deviation of sample means
E(s)= expected value of sample standard deviation 
and print in this format:
n = 2, k = 10, E(x) = , S(x) = , E(s) =