Agenda¶
- Numpy
- Introduction
- N-dimensional ndarray
- Create ndarray
- Inspect
- Reshaping
- Mathematics
- Pandas
- Introduction
- Structure
- Reading Files
- Inspect
- Descriptive Statistics
- Matplotlib
- Introduction
- Basic
- Plots
Numpy¶
In [1]:
import numpy as np
In [2]:
array_1d = np.array( [1,2,3] )
print('array_1d 的資料型態',type(array_1d))
print(array_1d)
In [3]:
array_2d = np.array( [ [1,2,3],
[4,5,6],
[7,8,9] ])
print('array_2d 的資料型態',type(array_2d))
print(array_2d)
Numpy¶
Create ndarray¶
- np.array(x) : 基本建立
- np.zeros(x) : x 是維度 ( 建立一個由 0 組成的 ndarray )
- np.ones(x) : x 是維度 ( 建立一個由 1 組成的 ndarray )
- np.empty(x) : x 是維度 ( 建立一個空的 ndarray )
- np.arange(start, stop, step) : 似 range()
- np.random.random(x) : x 是維度 (隨機產生0~1的值並組成ndarray)
- np.random.uniform(low, high, n) : 隨機產生 n 個介在 low~high 的值並服從 unifom
- np.random.normal(mu, sigma, n) : 隨機產生 n 個服從 normal( $\mu$ , $\sigma$ ) )
Exercise 希望將 Temperature_Taipei 中的所有溫度都轉成華氏
Temperature_Taipei = [22, 24, 19, 19, 30, 21, 25]
公式： $Fahrenheit = Celsius × (9/5) + 32$
In [4]:
Temperature_Taipei = [22, 24, 19, 19, 30, 21, 25]
In [5]:
print( list( map( lambda i : i * (9/5)+32, Temperature_Taipei ) ) )
In [6]:
Temperature_array = np.array( [22, 24, 19, 19, 30, 21, 25] )
Note 將 list 轉為 ndarray
In [7]:
Temperature_array = np.array( Temperature_Taipei )
In [8]:
Temperature_array_Fahrenheit = (Temperature_array)*(9/5)+32
print(Temperature_array_Fahrenheit)
In [9]:
print( np.zeros( (2,3) ) )
Note ( 2 ,3 ) ：表示一個 2 列 3 行二維的陣列
In [10]:
print( np.ones( (2,3,4) ) )
Note ( 2 ,3 ,4 ) ：表示一個 2 頁 3 列 4 行的三維陣列
In [11]:
print( np.empty( (2,2) ) )
Note 陣列中的元素為一個初始化值
In [12]:
multiplication = np.empty( (9 , 9) )
for i in range(1,10):
for j in range(1,10):
multiplication[(i-1),(j-1)]= i*j
In [13]:
print(multiplication)
In [14]:
print( np.arange(0,10,2) )
Review
In [15]:
print( list(range(0,10,2)) )
隨機產生 0~1 的數值
In [16]:
print( np.random.random( (2,3) ) )
隨機產生 0~10 的數值
In [17]:
print( np.random.random( (2,3) ) *10 )
In [18]:
unifom_distrbution = np.random.uniform(-1,1,25)
print(unifom_distrbution)
Note 設定 seed 可以固定每次 random 產生的結果
In [19]:
np.random.seed(100)
unifom_distrbution = np.random.uniform(-1,1,25)
print(unifom_distrbution)
In [20]:
normal_distrbution = np.random.normal( 0 , 1 , 20 )
print(normal_distrbution)
Note 設定 seed 可以固定每次 random 產生的結果
In [21]:
np.random.seed(100)
normal_distrbution = np.random.normal( 0 , 1 , 20 )
print(normal_distrbution)
Exercise 確認 array_2d 中元素個數
In [22]:
print( array_2d.size )
Review len
In [23]:
my_list = [0, 2, 4, 6, 8]
print( len(my_list) )
Exercise 確認 array_2d 中矩陣的外觀
In [24]:
print( array_2d.shape )
Exercise 確認 array_2d 的維度
In [25]:
print( array_2d.ndim )
Exercise 檢查 array_2d 中元素的型態
In [26]:
print( array_2d.dtype )
In [27]:
x_int = np.array([1 ,2 ,3] ,dtype=np.int) #利用 dtype 定義一個 int 的 ndarray
print(x_int)
In [28]:
print( "x_int 的資料型態", type(x_int) )
print( "---------------------------------" )
print( "x_int 元素的資料型態", x_int.dtype )
Note
- 默認是 64 位元的 int，也有 16, 32 位元
- 位元越小佔用的空間越小，若想要精確就必須用大一點。
In [29]:
x_float= np.array([1 ,2 ,3] ,dtype=np.float) #利用 dtype 定義一個 float 的 ndarray
print(x_float)
In [30]:
print( "x_float 的資料型態", type(x_float) )
print( "---------------------------------" )
print( "x_float 元素的資料型態", x_float.dtype )
In [31]:
x_str = np.array([1 ,2 ,3] ,dtype=np.str) #利用 dtype 定義一個 str 的 ndarray
print(x_str)
In [32]:
print( "x_str 的資料型態", type(x_str) )
print( "---------------------------------" )
print( "x_str 元素的資料型態", x_str.dtype )
In [33]:
weather_array = np.array([ 'Taipei' , 18.5 , 'rainy' ])
print(weather_array)
print( "---------------------------------" )
print( "weather_array 元素的資料型態", weather_array.dtype )
Exercise 隨機生成 一個名為 normal_random (20×1) 的ndarray，服從 normal(0,1)
In [34]:
normal_random = np.random.normal(0,1,20)
print(normal_random)
print( "---------------------------------" )
print( "normal_random 的維度", normal_random.ndim )
print( "---------------------------------" )
print( "normal_random 的外觀", normal_random.shape )
Exercise 利用 .reshape 將 normal_random 資料轉成 2d-ndarray(4X5)
In [35]:
normal_random_new = normal_random.reshape(4,5)
print(normal_random_new)
print( "---------------------------------" )
print( "normal_random_new 的外觀", normal_random_new.shape )
Exercise 將 array_2d 轉置
In [36]:
array_2d.T
Out[36]:
In [37]:
A = np.array([[1,2,3],[4,5,6],[7,8,9]])
print(A)
In [38]:
B = np.array([[1,1,1],[2,2,2],[3,3,3]])
print(B)
Exercise 將 A 乘上一個常數
In [39]:
A * 2
Out[39]:
Exercise 將 A , B 兩矩陣元素相乘
In [40]:
A * B
Out[40]:
Numpy¶
Mathematics¶
np.dot(A , B ): 兩ndarray相乘 (兩矩陣相乘)
source:http://www.stoimen.com/blog/wp-content/uploads/2012/11/4.-Rect-Matrix-Multiplication.png
Exercise 將 A , B 兩矩陣相乘
In [41]:
np.dot(A , B )
Out[41]:
Numpy¶
Mathematics¶
np.min(x): 找出ndarray中最小值np.max(x): 找出ndarray中最大值
Example
print(normal_distrbution)
[-1.74976547 0.3426804 1.1530358 -0.25243604 0.98132079
0.51421884 0.22117967 -1.07004333 -0.18949583 0.25500144
-0.45802699 0.43516349 -0.58359505 0.81684707 0.67272081
-0.10441114 -0.53128038 1.02973269 -0.43813562 -1.11831825]Exercise 分別找出 normal_distrbution 中最大、最小值
In [42]:
print("normal_distrbution 的最小值：", np.min(normal_distrbution))
print("normal_distrbution 的最大值：", np.max(normal_distrbution))
Exercise 分別找出 normal_distrbution 的總和、平均數、標準差、中位數
In [43]:
print("normal_distrbution 的總和：", np.sum(normal_distrbution))
print("normal_distrbution 的平均數：", np.mean(normal_distrbution))
print("normal_distrbution 的標準差：", np.std(normal_distrbution))
print("normal_distrbution 的中位數：", np.median(normal_distrbution))
Pandas¶
In [44]:
import pandas as pd
source:In [45]:
Taipei_array = np.array([17.0,8.3,20.1,22.6,27.0,27.8,30.1,30.3,29.4,27.1,24.0,17.5,])
print("Taipei_array 的資料型態", type(Taipei_array))
print( "---------------------------------" )
print(Taipei_array)
In [46]:
Taipei_series = pd.Series(Taipei_array)
print("Taipei_series 的資料型態", type(Taipei_series))
print( "---------------------------------" )
print(Taipei_series)
In [47]:
month = [ 'FEB', 'JAN', 'MAR', 'APR', 'MAY', 'JUN', 'JUL', 'AUG', 'SEP', 'OCT', 'NOV', 'DEC']
In [48]:
Taipei_series = pd.Series( Taipei_array ,
index = month )
In [49]:
print(Taipei_series)
In [50]:
print(Taipei_series['FEB'])
In [51]:
Taipei_dic = {'FEB': 17.0, 'JAN': 8.3, 'MAR': 20.1, 'APR': 22.6, 'MAY': 27.0, 'JUN': 27.8, 'JUL': 30.1, 'AUG': 30.3, 'SEP': 29.4, 'OCT': 27.1, 'NOV': 24.0, 'DEC': 17.5}
print("Taipei_dic 的資料型態", type(Taipei_dic))
print( "---------------------------------" )
print(Taipei_dic)
Note Dictionary 的 Key 會變成 index
In [52]:
Taipei_series = pd.Series(Taipei_dic)
print("Taipei_series 的資料型態", type(Taipei_series))
print( "---------------------------------" )
print(Taipei_series)
In [53]:
Temperature = np.array([[17.0,8.3,20.1,22.6,27.0,27.8,30.1,30.3,29.4,27.1,24.0,17.5],
[21.1,21.7,22.9,25.7,28.4,28.4,29.4,30.2,29.7,28.9,26.9,21.6]])
print("Temperature 的資料型態", type(Temperature))
print( "---------------------------------" )
print("Temperature 的外觀", np.shape(Temperature))
print( "---------------------------------" )
print(Temperature)
In [54]:
Temperature_dataframe = pd.DataFrame(Temperature)
print("Temperature_dataframe 的資料型態", type(Temperature_dataframe))
print( "---------------------------------" )
print(Temperature_dataframe)
In [55]:
Temperature_dataframe = pd.DataFrame(Temperature,
index=['Taipei','Kaohsiung'],
columns = month)
In [56]:
print(Temperature_dataframe)
In [57]:
Temperature_dic = {'FEB': [17.0,21.1], 'JAN': [8.3,21.7], 'MAR': [20.1,22.9], 'APR': [22.6,25.7], 'MAY': [27.0,28.4], 'JUN': [27.8,28.4],'JUL': [30.1,29.4], 'AUG': [30.3,30.2], 'SEP': [29.4,29.7], 'OCT': [27.1,28.9], 'NOV':[24.0,26.9], 'DEC': [17.5,21.6]}
print("Temperature_dic 的資料型態", type(Temperature_dic))
print( "---------------------------------" )
print(Temperature_dic)
Note Dictionary 的 Key 會變成 column name
In [58]:
Temperature_dataframe = pd.DataFrame(Temperature_dic)
print("Temperature_dataframe 的資料型態", type(Temperature_dataframe))
print( "---------------------------------" )
print(Temperature_dataframe)
In [59]:
Temperature_dataframe = pd.DataFrame(Temperature,
columns = month)
In [60]:
print(Temperature_dataframe)
In [61]:
Temperature_dataframe = pd.DataFrame(Temperature,
columns = month,
index=['Taipei','Kaohsiung'])
In [62]:
print(Temperature_dataframe)
In [63]:
Taipei = [17.0,8.3,20.1,22.6,27.0,27.8,30.1,30.3,29.4,27.1,24.0,17.5]
Kaohsiung = [21.1,21.7,22.9,25.7,28.4,28.4,29.4,30.2,29.7,28.9,26.9,21.6]
Quality = ['Good','Good','Bad','Bad','Bad','Good','Bad','Bad','Good','Bad','Good','Good']
Temperature = { 'Taipei' : Taipei ,
'Kaohsiung' : Kaohsiung ,
'Quality' : Quality }
Temperature_dataframe = pd.DataFrame(Temperature, index = month)
print(Temperature_dataframe)
In [64]:
import os
os.getcwd()
Out[64]:
Pandas¶
Reading Files¶
.read_csv( ): 讀取 csv 格式資料
2002 - 2016 年國民出國目的地人數統計
source:政府開放資料 http://data.gov.tw/node/7325
In [65]:
Aboard = pd.read_csv('/Users/hsinyuchan/Desktop/TKUXPython/Data/Aboard.csv',encoding='big5')
In [66]:
Aboard.head()
Out[66]:
Pandas¶
Reading Files¶
.read_excel( ): 讀取 excel 資料
100-104 年各縣市消防人力人數統計
source:內政部統計查詢網 http://statis.moi.gov.tw/micst/stmain.jsp?sys=100
In [67]:
Fire = pd.read_excel('/Users/hsinyuchan/Desktop/TKUXPython/Data/Fire.xlsx')
In [68]:
Fire.head()
Out[68]:
Pandas¶
Reading Files¶
.read_json( ): 讀取 JSON 格式資料
臺北市施工資訊
source:台北市政府資料開放平台 http://data.taipei/opendata/datalist/datasetMeta?oid=4d29818c-a3ee-425d-b88a-22ac0c24c712
In [69]:
construction = pd.read_json('/Users/hsinyuchan/Desktop/TKUXPython/Data/construction.json')
In [70]:
construction.head(1)
Out[70]:
In [71]:
import codecs
In [72]:
construction = pd.read_json(codecs.open('/Users/hsinyuchan/Desktop/TKUXPython/Data/construction.json', 'r', 'utf-8'))
In [73]:
Temperature_dataframe
Out[73]:
Exercise 查看 Temperature_dataframe 的前五筆資料
In [74]:
Temperature_dataframe.head(5)
Out[74]:
Exercise 查看 Temperature_dataframe2 的後三筆資料
In [75]:
Temperature_dataframe.tail(3)
Out[75]:
Exercise 確認 Temperature_dataframe 的欄位資料類型
In [76]:
Temperature_dataframe.info()
source: 
Exercise 查看 Temperature_dataframe 中連續型資料的基本統計敘述
In [77]:
Temperature_dataframe.describe()
Out[77]:
Exercise 查看 Temperature_dataframe 中類別型資料的次數
In [78]:
Temperature_dataframe['Quality'].value_counts()
Out[78]:
Matplotlib¶
In [79]:
import matplotlib.pyplot as plt
In [80]:
Year = ['2014','2014','2014','2014','2014','2014','2014','2014','2014','2014','2014','2014',
'2015','2015','2015','2015','2015','2015','2015','2015','2015','2015','2015','2015',
'2016','2016','2016','2016','2016','2016','2016','2016','2016','2016','2016','2016']
Month = ['JAN','FEB','MAR','APR','MAY','JUN','JUL','AUG','SEP','OCT','NOV','DEC',
'JAN','FEB','MAR','APR','MAY','JUN','JUL','AUG','SEP','OCT','NOV','DEC',
'JAN','FEB','MAR','APR','MAY','JUN','JUL','AUG','SEP','OCT','NOV','DEC']
Season = ['Winter','Winter','Spring','Spring','Spring','Summer','Summer','Summer','Fall','Fall','Fall','Winter',
'Winter','Winter','Spring','Spring','Spring','Summer','Summer','Summer','Fall','Fall','Fall','Winter',
'Winter','Winter','Spring','Spring','Spring','Summer','Summer','Summer','Fall','Fall','Fall','Winter']
Taipei = [16.8,16.5,18.9,22.5,25.2,28.0,30.5,30.2,29.7,24.7,22.3,16.5,
16.7,20.8,18.9,22.7,26.1,30.0,30.0,28.6,27.4,25.2,23.5,18.9,
16.2,15.5,17.5,24.0,27.1,29.4,30.3,30.1,27.8,27.0,22.6,20.1]
Taichung = [16.4,17.0,19.8,24.1,25.8,28.6,30.1,28.9,29.2,25.6,23.1,17.1,
17.1,18.1,20.7,24.4,26.9,29.7,29.2,28.0,27.8,26.2,24.3,19.6,
16.8,16.4,18.3,24.9,27.6,28.8,29.4,28.9,27.9,27.5,23.4,20.4]
Kaohsiung = [19.5,20.3,22.6,25.9,27.8,29.2,30.3,29.1,29.5,27.2,25.2,20.2,
19.9,17.3,23.6,26.2,28.3,30.6,29.5,28.8,28.8,27.7,26.2,22.4,
19.3,19.6,21.6,26.9,28.9,29.7,30.2,29.4,28.4,28.4,25.7,22.9]
Temperature_table = { "Year" : Year,
"Month" : Month,
"Season" : Season,
"Taipei" : Taipei,
"Taichung" : Taichung,
"Kaohsiung" : Kaohsiung }
Temperature = pd.DataFrame(Temperature_table)
print(Temperature.shape)
Temperature.head()
Out[80]:
In [81]:
Taipei_Season = Temperature['Taipei'].groupby(Temperature['Season']).mean()
Taipei_Season
Out[81]:
Exercise 想知道 Temperature 資料中 台北市 及 台中市 溫度的關係
In [82]:
plt.scatter(Temperature['Taipei'], Temperature['Taichung'])
plt.show()
Exercise 想知道 Temperature 資料中 台北市 及 台中市 溫度的關係，加入標籤及標題
In [83]:
x = Temperature['Taipei']
y = Temperature['Taichung']
plt.scatter(x, y)
plt.xlabel("Taipei")
plt.ylabel("Taichung")
plt.title("Taipei VS Taichung")
plt.show()
In [84]:
x = np.arange(-2.0, 2.0, 0.01)
y = np.sin(2 * np.pi * x)
plt.plot(x, y)
plt.xlabel("x")
plt.ylabel("y")
plt.title("f(x) = sin(x)")
plt.show()
Exercise¶
Q. Line Plot¶
$$ f(x) = x^2 ; -2 \le x \le 2 $$
Answers¶
In [85]:
x = np.arange(-2.0, 2.0, 0.01)
y = x**2
plt.plot(x, y)
plt.xlabel("x")
plt.ylabel("y")
plt.title("$f(x) = x^2$")
plt.grid(True)
plt.show()
In [86]:
normal_data = np.random.normal(size = 10000)
plt.hist(normal_data)
plt.title("Normal distribution")
plt.show()
In [87]:
uniform_data = np.random.uniform(size = 10000)
plt.hist(uniform_data)
plt.title("Uniform distribution")
plt.show()
In [88]:
normal_data = np.random.normal(size = 10000)
uniform_data = np.random.uniform(size = 10000)
plt.subplot(1, 2, 1)
plt.hist(normal_data)
plt.title("Normal distribution")
plt.subplot(1, 2, 2)
plt.hist(uniform_data)
plt.title("Uniform distribution")
plt.show()
Exercise 利用 Taipei_Season 畫一個，台北在不同季節下的溫度變化
In [89]:
season_cut = range(len(Taipei_Season))
plt.bar(season_cut , Taipei_Season, color="blue", align = "center")
plt.xticks(season_cut , Taipei_Season.index)
plt.show()
