ProgrammerSought

Numpy

Numerical Python

Fundamental package for high performance scientific computing and data analysis

Provides multi-dimensional array objects with vector operation capabilities

Pandas

Based on Numpy, it provides high-performance matrix operations

Has more convenient methods used in matrix calculations

one-dimensional array

Pandas:Series

Numpy:ndarray

Similarities between ndarray and Python List

Access elements by position

l[0]、l[2:5]、l[:3]、l[2:]

cycle:

for item in l:

   xxx

Difference between ndarray and Python List

1. Python List elements can be combined with any type, and ndarray element types must be the same (Numpy will automatically do type conversion when they are not the same)

2. ndaarray has mean(), std() and more built-in functions related to mathematical calculations

3.ndarray can be more convenient to operate on multi-dimensional arrays

import numpy as np
import pandas as pd

print(type(np.array([1, 2, 3])))
print(type(pd.Series([1, 2, 3])))
hours = np.array(['12AM', '1AM', '2AM', '3AM',
                  '4AM', '5AM', '6AM', '7AM',
                  '8AM', '9AM', '10AM', '11AM',
                  '1PM', '2PM', '3PM', '4PM',
                  '5PM', '6PM', '7PM', '8PM',
                  '9PM', '10PM', '11PM', '12PM'])
Temps = np.array([-1, -1, -1, -1,
                  -1, -1, -1, -1,
                  -1, -1, -1, -1,
                  -1, -1, -1, -1,
                  -1, -1, -1, -1,
                  -1, -1, -1, -1,
                  ])
print(hours[14])
print(hours[2:4])
print(Temps[2:4])
for i in hours:
    print(i, end=" ")
print()
print(Temps.dtype)
print(np.array([1,2,3]).dtype)
print(np.array(['HELLO',1,True]).dtype)
for i in np.array(['Hello',1,True]):
    print(type(i))

print(np.array([[1,2,3],[1,2,3],[1,2,3]]).sum())
print(Temps.sum())
print(Temps.std())
print(Temps.mean())

Find the highest temperature of the day and its corresponding time

import numpy as np
import pandas as pd

print(type(np.array([1, 2, 3])))
print(type(pd.Series([1, 2, 3])))
hours = np.array(['12AM', '1AM', '2AM', '3AM',
                  '4AM', '5AM', '6AM', '7AM',
                  '8AM', '9AM', '10AM', '11AM',
                  '1PM', '2PM', '3PM', '4PM',
                  '5PM', '6PM', '7PM', '8PM',
                  '9PM', '10PM', '11PM', '12PM'])
Temps = np.array([-9, 20, -15, -16,
                  -1, -7, 7, 8,
                  -13, -5, -14, -1,
                  -12, -1, -6, -1,
                  -2, 6, -1, 4,
                  -8, -1, -1, -3,
                  ])
def getMaxTemp(hours,temps):
    max_temp = temps.max()
    max_hour = hours[temps.argmax()]
    return (max_temp,max_hour)
print(getMaxTemp(hours,Temps))