[python] Frequently used techniques in machine learning

Data frame filled with all 0s

import pandas as pd
import numpy as np

rows = 4
cols = 4 
df = pd.DataFrame(np.zeros((rows, cols)))

numpy.float Get the number of decimal places of an array element of type 64

I used it when I wanted to split the element extracted from the numpy array and check the number of decimal places. Since split is a method of type str, convert it to str and then get it.

import numpy as np

narr = np.array([1.3334, 5.3343, 7.3322])

n_to_str = np.array2string(x[0])

# 4

Concatenate data frames

When you want to connect to the far right

a = [1,2,3]
b = [1,2,3]

bf = pd.DataFrame()
bf['a'] = a
bf['b'] = b
	a	b
0	1	1
1	2	2
2	3	3
a = [1,2,3]
b = [1,2,3]

bf = pd.DataFrame(a, columns=['a'])
bf.insert(1, "b", b)
	a	b
0	1	1
1	2	2
2	3	3

Connected together

a = [1,2,3]
b = [1,2,3]
c = [1,2,3]

cols = {

da = pd.DataFrame(cols)

#Connected horizontally
print(pd.concat([da,da], axis=1, ignore_index=True))
#Connected vertically
# ignore_If index is not specified as True, it will be 012012.
print(pd.concat([da,da], axis=0, ignore_index=True))

   0  1  2  3  4  5
0  1  1  1  1  1  1
1  2  2  2  2  2  2
2  3  3  3  3  3  3

   a  b  c
0  1  1  1
1  2  2  2
2  3  3  3
3  1  1  1
4  2  2  2
5  3  3  3

Convert strings to datetime, timestamp format

It is a technique to convert to a format that can be handled by python when calculating date and time data.

Converting to timestamp makes it easier to handle when seconds, minutes, and hours move forward.

An example of trying to calculate without doing anything

#Example) 1:59:50 + 0:00::20 = 2:00:10
#When not converting to timestamp

hour = 1
minute = 59
sec = 50

add_hour = 0
add_minute = 0
add_sec = 20

if sec + add_sec > 60 or minute + add_minute > 60 or hour + add_hour > 24:
    #Carrying process
    #Processing when it does not move up

Datetime string,Convert to timestamp

from datetime import datetime

s = "2020.07.10 22:59:59"

date = datetime.strptime(s, "%Y.%m.%d %H:%M:%S")
# 2020-07-10 22:59:59
timestamp = datetime.timestamp(date)
# 1594389599.0

Return the timestamp value put in the list etc. to the character string

Put the timestamp in a list and then return it to a string

from datetime import datetime

t_list = [1594177973.0,1594177974.0,1594177975.0]
# float

#Convert to datetime type and then to string
# '12:12:53'

Addition and subtraction of time

You can add and subtract to a datetime.datetime object using datetime.timedelta

Supports calculations for weeks, days, hours, minutes, seconds

import datetime

#Current time
now = datetime.datetime.now()
# 2020-07-11 18:19:47.149523

#Add 10 seconds to the current time
now_add_10sec = now + datetime.timedelta(seconds=10)
# 2020-07-11 18:19:57.149523

moving average

Use numpy convolve. It's faster than pandas rollin, and the code is short and easy to understand.

period = 3 #Average period
data = np.arange(1,10)

# mode = { same, valid, full }You can choose from
# same:The number of elements in the calculation result is the same size(not recommended)
# valid:There are many considerations, but the size is(period-1)Be scraped(Recommendation)
# full:Increased size (deprecated))

average = np.convolve(data, np.ones(period)/period, mode='valid')
print([np.nan]*(period-1) + average.tolist()) #Put Nan in the scraped area
[1 2 3 4 5 6 7 8 9]
[2. 3. 4. 5. 6. 7. 8.]
[nan, nan, 2.0, 3.0, 3.9999999999999996, 5.0, 6.0, 7.0, 8.0]

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