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python statistics

Innocent Iguana answered on August 30, 2022 Popularity 8/10 Helpfulness 4/10

answer python statistics

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python statistics

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import statistics
import numpy as np
# mode 
statistics.mode(df['col'])
# median 
statistics.median(df['col'])
# sample mean 
statistics.mean(df['col'])
# sample variance 
np.var(df['col'], ddof=1)
# sample standard deviation 
np.std(df['col'], ddof=1)
# population variance 
np.var(df['col'], ddof=0)
# population standard deviation 
np.std(df['col'], ddof=0)
# quantile 
np.quantile(df['col'], [0, 0.25, 0.5, 0.75, 1])
# iqr 
scipy.stats.iqr(df['col'])
from scipy.stats import norm
# Sort the values in the column for CDF and inverse CDF
sorted_col = np.sort(df['col'])
# Calculate PDF using a probability distribution (e.g., normal distribution)
pdf_values = norm.pdf(sorted_col, loc=df['col'].mean(), scale=df['col'].std())
# Calculate CDF
cdf_values = norm.cdf(sorted_col, loc=df['col'].mean(), scale=df['col'].std())
# Calculate Inverse CDF (Quantiles)
quantiles_values = norm.ppf(np.linspace(0, 1, len(sorted_col)), loc=df['col'].mean(), scale=df['col'].std())

# Visualize PDF, CDF, Inverse CDF
result_df = pd.DataFrame({
    'col': sorted_col,
    'PDF': pdf_values,
    'CDF': cdf_values,
    'Quantiles': quantiles_values
})
sns.lineplot(x='col', y='PDF', data=result_df)
sns.lineplot(x='col', y='CDF', data=result_df)
sns.scatterplot(x='col', y='Quantiles', data=result_df)
plt.show()

Popularity 8/10 Helpfulness 4/10 Language python

Source: Grepper

Tags: python statistics

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Contributed on Dec 11 2023

Innocent Iguana

0 Answers Avg Quality 2/10

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import statistics

# print harmonic mean - calculates central location of the list
print(statistics.harmonic_mean([10, 20, 30]))

# print mean - calculates average of the list
print(statistics.mean([10, 20, 30]))

# print median - returns middle value of the list
print(statistics.median([10, 20, 30]))

# print median high value - returns if the list is an even number the left median value is returned
print(statistics.median_low([10, 20, 30, 40]))

# print median high value - returns if the list is an even number the right median value is returned
print(statistics.median_high([10, 20, 30, 40]))

# print the mode - calculates the central tendency of the given numeric or nominal data
print(statistics.mode([10, 20, 30, 40]))

# print the standard deviation from an entire population - a low standard deviation number means the data is more
# closely related
print(statistics.pstdev([10, 20, 30, 40]))
print(statistics.pstdev([1, 2, 1]))

# print the standard deviation from a sample of data - a low standard deviation number means the data is more
# closely related
print(statistics.stdev([1, 2, 1]))

# print the variance from a sample data - A low variance indicates that the data is less spread out
print(statistics.variance([1, 2, 3, 4, 5]))

# print the variance of an entire population - A low variance indicates that the data is less spread out
print(statistics.pvariance([1, 2, 3, 4, 5]))

# print quantiles - Divide data into n continuous intervals with equal probability. Returns a list of n - 1 cut
# points separating the intervals.
print(statistics.quantiles([1, 2, 3]))

# print median of grouped continuous data - returns a float representing the median of a grouped continuous data,
# calculated as the 50th percentile
print(statistics.median_grouped([10, 20, 30]))

Popularity 9/10 Helpfulness 3/10 Language python

Source: codefreelance.net

Tags: python py

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Contributed on Aug 30 2022

Jerome Scott

0 Answers Avg Quality 2/10

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