Low, Strong, No Autocorrelation graphs

 Below code snippet can give idea on the data

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from statsmodels.graphics.tsaplots import plot_acf

from pandas.plotting import autocorrelation_plot

# Generate white noise data

np.random.seed(42)

data_low_corr = np.random.randn(500)

df_low_corr = pd.DataFrame(data_low_corr, columns=['value'])

# Plot autocorrelation

plot_acf(df_low_corr)

plt.show()

autocorrelation_plot(df_low_corr)

plt.show()

pd.plotting.lag_plot(df_low_corr, lag = 1)

plot_acf(df_low_corr, alpha = 0.05)

Now below gives snippet for no correlation 

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from statsmodels.graphics.tsaplots import plot_acf

# Generate random data

np.random.seed(42)

data_no_corr = np.random.uniform(size=100)

df_no_corr = pd.DataFrame(data_no_corr, columns=['value'])

# Plot autocorrelation

# plot_acf(df_no_corr)

# plt.show()

from pandas.plotting import autocorrelation_plot

autocorrelation_plot(df_no_corr)

plt.show()

pd.plotting.lag_plot(df_no_corr, lag = 1)

plot_acf(df_no_corr, alpha = 0.05)

The plots look like the below 

Below is with High correlation 

nt_array = pd.array([1, 2, 3, 4, 5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25], dtype='int')

print(int_array)

print()

df_no_corr = pd.DataFrame(int_array, columns=['value'])

# Plot autocorrelation

# plot_acf(df_no_corr)

# plt.show()

from pandas.plotting import autocorrelation_plot

autocorrelation_plot(df_no_corr)

plt.show()

pd.plotting.lag_plot(df_no_corr, lag = 1)

plot_acf(df_no_corr, alpha = .05)