Pandas - The Algorithms

import pandas as pd
import numpy as np


def header(msg):
  print('-'*50)
  print('['+msg+']')
  
df = pd.DataFrame(
[['Jan',58,42,74,22,2.95],
['Feb',61,45,78,26,3.02],
['Mar',65,48,84,25,2.34],
['Apr',67,50,92,28,1.02],
['May',71,53,98,35,0.48],
['Jun',75,56,107,41,0.11],
['Jul',77,58,105,44,0.0],
['Aug',77,59,102,43,0.03],
['Sep',77,57,103,40,0.17],
['Oct',73,54,96,34,0.81],
['Nov',64,48,84,30,1.7],
['Dec',58,42,73,21,2.56]],
index = [0,1,2,3,4,5,6,7,8,9,10,11],
columns = ['month','avg_high','avg_low','record_high','record_low','avg_precipitation'])

print(df)
df.to_csv('foo.csv')

   month  avg_high  avg_low  record_high  record_low  avg_precipitation
0    Jan        58       42           74          22               2.95
1    Feb        61       45           78          26               3.02
2    Mar        65       48           84          25               2.34
3    Apr        67       50           92          28               1.02
4    May        71       53           98          35               0.48
5    Jun        75       56          107          41               0.11
6    Jul        77       58          105          44               0.00
7    Aug        77       59          102          43               0.03
8    Sep        77       57          103          40               0.17
9    Oct        73       54           96          34               0.81
10   Nov        64       48           84          30               1.70
11   Dec        58       42           73          21               2.56

header("2.df.head()")
print(df.head())

header("3.df.tail()")
print(df.head())

--------------------------------------------------
[2.df.head()]
  month  avg_high  avg_low  record_high  record_low  avg_precipitation
0   Jan        58       42           74          22               2.95
1   Feb        61       45           78          26               3.02
2   Mar        65       48           84          25               2.34
3   Apr        67       50           92          28               1.02
4   May        71       53           98          35               0.48
--------------------------------------------------
[3.df.tail()]
  month  avg_high  avg_low  record_high  record_low  avg_precipitation
0   Jan        58       42           74          22               2.95
1   Feb        61       45           78          26               3.02
2   Mar        65       48           84          25               2.34
3   Apr        67       50           92          28               1.02
4   May        71       53           98          35               0.48

header("4.df.dtypes()")
print(df.dtypes)

--------------------------------------------------
[4.df.dtypes()]
month                 object
avg_high               int64
avg_low                int64
record_high            int64
record_low             int64
avg_precipitation    float64
dtype: object

header("5.df.index()")
print(df.index)

--------------------------------------------------
[5.df.index()]
Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], dtype='int64')

header("6.df.columns()")
print(df.columns)

--------------------------------------------------
[6.df.columns()]
Index(['month', 'avg_high', 'avg_low', 'record_high', 'record_low',
       'avg_precipitation'],
      dtype='object')

header("7.df.values()")
print(df.values)

--------------------------------------------------
[7.df.values()]
[['Jan' 58 42 74 22 2.95]
 ['Feb' 61 45 78 26 3.02]
 ['Mar' 65 48 84 25 2.34]
 ['Apr' 67 50 92 28 1.02]
 ['May' 71 53 98 35 0.48]
 ['Jun' 75 56 107 41 0.11]
 ['Jul' 77 58 105 44 0.0]
 ['Aug' 77 59 102 43 0.03]
 ['Sep' 77 57 103 40 0.17]
 ['Oct' 73 54 96 34 0.81]
 ['Nov' 64 48 84 30 1.7]
 ['Dec' 58 42 73 21 2.56]]

header("8.df.describe()")
df.describe()

--------------------------------------------------
[8.df.describe()]

	avg_high	avg_low	record_high	record_low	avg_precipitation
count	12.000000	12.000000	12.000000	12.000000	12.000000
mean	68.583333	51.000000	91.333333	32.416667	1.265833
std	7.366488	6.060303	12.323911	8.240238	1.186396
min	58.000000	42.000000	73.000000	21.000000	0.000000
25%	63.250000	47.250000	82.500000	25.750000	0.155000
50%	69.000000	51.500000	94.000000	32.000000	0.915000
75%	75.500000	56.250000	102.250000	40.250000	2.395000
max	77.000000	59.000000	107.000000	44.000000	3.020000

header("8.df.sort_values()")
df.sort_values("record_high" , ascending= "False")

--------------------------------------------------
[8.df.sort_values()]

	month	avg_high	avg_low	record_high	record_low	avg_precipitation
11	Dec	58	42	73	21	2.56
0	Jan	58	42	74	22	2.95
1	Feb	61	45	78	26	3.02
2	Mar	65	48	84	25	2.34
10	Nov	64	48	84	30	1.70
3	Apr	67	50	92	28	1.02
9	Oct	73	54	96	34	0.81
4	May	71	53	98	35	0.48
7	Aug	77	59	102	43	0.03
8	Sep	77	57	103	40	0.17
6	Jul	77	58	105	44	0.00
5	Jun	75	56	107	41	0.11

df.avg_low

0     42
1     45
2     48
3     50
4     53
5     56
6     58
7     59
8     57
9     54
10    48
11    42
Name: avg_low, dtype: int64

df['avg_low']

0     42
1     45
2     48
3     50
4     53
5     56
6     58
7     59
8     57
9     54
10    48
11    42
Name: avg_low, dtype: int64

df[2:4]

	month	avg_high	avg_low	record_high	record_low	avg_precipitation
2	Mar	65	48	84	25	2.34
3	Apr	67	50	92	28	1.02

df[['avg_low','avg_high','record_high']]

	avg_low	avg_high	record_high
0	42	58	74
1	45	61	78
2	48	65	84
3	50	67	92
4	53	71	98
5	56	75	107
6	58	77	105
7	59	77	102
8	57	77	103
9	54	73	96
10	48	64	84
11	42	58	73

df.iloc[5:8 , [0,3]]  #it will use as array

	month	record_high
5	Jun	107
6	Jul	105
7	Aug	102

df.loc[5:8 , ['avg_low','avg_high','record_high']]

	avg_low	avg_high	record_high
5	56	75	107
6	58	77	105
7	59	77	102
8	57	77	103

df.loc[9, ['avg_low','avg_high','record_high']]

avg_low        54
avg_high       73
record_high    96
Name: 9, dtype: object

df.iloc[3:5,[0,3]]

	month	record_high
3	Apr	92
4	May	98

	avg_low	avg_high	record_high
0	42	58	74
1	45	61	78
2	48	65	84
3	50	67	92
4	53	71	98
5	56	75	107
6	58	77	105
7	59	77	102
8	57	77	103
9	54	73	96
10	48	64	84
11	42	58	73

	avg_low	avg_high	record_high
0	42	58	74
1	45	61	78
2	48	65	84
3	50	67	92
4	53	71	98
5	56	75	107
6	58	77	105
7	59	77	102
8	57	77	103
9	54	73	96
10	48	64	84
11	42	58	73

	avg_low	avg_high	record_high
0	42	58	74
1	45	61	78
2	48	65	84
3	50	67	92
4	53	71	98
5	56	75	107
6	58	77	105
7	59	77	102
8	57	77	103
9	54	73	96
10	48	64	84
11	42	58	73