Python 기술 통계

Descriptive Statistics¶

In [ ]:

import warnings
warnings.filterwarnings('ignore')

Load 'tips.csv' Data¶

In [ ]:

import seaborn as sns

DF = sns.load_dataset('tips')

I. pandas¶

1) DataFrame Information¶

In [ ]:

DF.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 244 entries, 0 to 243
Data columns (total 7 columns):
 #   Column      Non-Null Count  Dtype   
---  ------      --------------  -----   
 0   total_bill  244 non-null    float64 
 1   tip         244 non-null    float64 
 2   sex         244 non-null    category
 3   smoker      244 non-null    category
 4   day         244 non-null    category
 5   time        244 non-null    category
 6   size        244 non-null    int64   
dtypes: category(4), float64(2), int64(1)
memory usage: 7.4 KB

In [ ]:

DF.head()

Out[ ]:

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

2) .describe( )¶

In [ ]:

DF.tip.describe()

Out[ ]:

count    244.000000
mean       2.998279
std        1.383638
min        1.000000
25%        2.000000
50%        2.900000
75%        3.562500
max       10.000000
Name: tip, dtype: float64

3) .sum( )¶

In [ ]:

%precision 5

DF.tip.sum()

Out[ ]:

731.5799999999999

4) .mean( )¶

In [ ]:

DF.tip.mean()

Out[ ]:

2.99827868852459

5) .min( )¶

In [ ]:

DF.tip.min()

Out[ ]:

1.0

6) .quantile(q = 0.25)¶

In [ ]:

DF.tip.quantile(q = 0.25)

Out[ ]:

2.0

7) .median( )¶

In [ ]:

DF.tip.median()

Out[ ]:

2.9

8) .quantile(q = 0.75)¶

In [ ]:

DF.tip.quantile(q = 0.75)

Out[ ]:

3.5625

9) .max( )¶

In [ ]:

DF.tip.max()

Out[ ]:

10.0

10) .var(ddof = 0)¶

• Dynamic Degrees Of Freedom(default = 1)

• 0: 모집단

• 1: 표본집단

In [ ]:

DF.tip.var(ddof = 0)

Out[ ]:

1.9066085124966412

In [ ]:

DF.tip.var(ddof = 1)

Out[ ]:

1.914454638062471

11) .std(ddof = 0)¶

In [ ]:

DF.tip.std(ddof = 0)

Out[ ]:

1.3807999538298954

In [ ]:

DF.tip.std(ddof = 1)

Out[ ]:

1.3836381890011822

II. numpy¶

In [ ]:

import numpy as np

1) Casting to Array¶

In [ ]:

AR = np.array(DF.tip)

In [ ]:

AR[:5]

Out[ ]:

array([1.01, 1.66, 3.5 , 3.31, 3.61])

2) .sum( )¶

In [ ]:

AR.sum()

Out[ ]:

731.5799999999999

3) .mean( )¶

In [ ]:

AR.mean()

Out[ ]:

2.99827868852459

4) .min( )¶

In [ ]:

AR.min()

Out[ ]:

1.0

5) .max( )¶

In [ ]:

AR.max()

Out[ ]:

10.0

6) .var(ddof = 0)¶

• default = 0

In [ ]:

AR.var(ddof = 0)

Out[ ]:

1.9066085124966412

7) .std(ddof = 0)¶

In [ ]:

AR.std(ddof = 0)

Out[ ]:

1.3807999538298954

III. scipy¶

In [ ]:

import scipy as sp

1) .sum( )¶

In [ ]:

sp.sum(DF['tip'])

Out[ ]:

731.5799999999999

2) .mean( )¶

In [ ]:

sp.mean(DF['tip'])

Out[ ]:

2.99827868852459

3) .amin( )¶

In [ ]:

sp.amin(DF['tip'])

Out[ ]:

1.0

4) .median( )¶

In [ ]:

sp.median(DF['tip'])

Out[ ]:

2.9

5) .amax( )¶

In [ ]:

sp.amax(DF['tip'])

Out[ ]:

10.0

6) .var(ddof = 0)¶

• default = 0

In [ ]:

sp.var(DF['tip'], ddof = 0)

Out[ ]:

1.9066085124966412

7) .std(ddof = 0)¶

In [ ]:

sp.std(DF['tip'], ddof = 0)

Out[ ]:

1.3807999538298954

8) .stats.mode( )¶

• DF.tip.value_counts( )

In [ ]:

DF.tip.value_counts()

Out[ ]:

2.00    33
3.00    23
4.00    12
5.00    10
2.50    10
        ..
4.34     1
1.56     1
5.20     1
2.60     1
1.75     1
Name: tip, Length: 123, dtype: int64

In [ ]:

sp.stats.mode(DF['tip'])

Out[ ]:

ModeResult(mode=array([2.]), count=array([33]))

값 '2'가 33번으로 가장 많이 나옴 (대표값/최빈값)

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End Of Document¶

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