Descriptive Statistics

DEMO_DATA_ROOT = "../../../RepositoryData/data"

import pandas as pd
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

Univariate Statistics

UHM = pd.read_table(DEMO_DATA_ROOT+"/gries_sflwr/_inputfiles/03-1_uh(m).csv")
UHM

	CASE	SEX	FILLER	GENRE	LENGTH
0	1	male	uhm	monolog	1014
1	2	female	silence	monolog	1188
2	3	female	silence	dialog	889
3	4	female	uhm	dialog	265
4	5	male	uh	dialog	465
...	...	...	...	...	...
995	996	male	silence	dialog	751
996	997	female	uhm	dialog	1005
997	998	female	uhm	monolog	568
998	999	female	uh	dialog	984
999	1000	female	uh	dialog	1521

1000 rows × 5 columns

UHM.value_counts(UHM['FILLER'])

FILLER
uh         394
silence    332
uhm        274
dtype: int64

UHM.value_counts(UHM['FILLER'], normalize=True)

FILLER
uh         0.394
silence    0.332
uhm        0.274
dtype: float64

def ecdf(data):
    x = np.sort(data)
    y = np.arange(1, len(x)+1)/len(x) # percentiles
    return(x,y)

ecdf(UHM.value_counts(UHM['FILLER']))

(array([274, 332, 394]), array([0.33333333, 0.66666667, 1.        ]))