物件

物件導向1
一個物件（Object）可以包含屬性（Attribute）與方法（Method）

import pandas as pd

name = ["蒙其·D·魯夫", "羅羅亞·索隆", "娜美", "騙人布", "賓什莫克·香吉士", "多尼多尼·喬巴", "妮可·羅賓", "佛朗基", "布魯克"] 

age = [19, 21, 20, 19, 21, 17, 30, 36, 90] 

# 建立 dict straw_hat_dict = {"name": name, "age": age } 

# 建立 data frame 

straw_hat_df = pd.DataFrame(straw_hat_dict) 

print(straw_hat_df.dtypes) # straw_hat_df 有 dtypes 屬性 

print(straw_hat.df.head()) # straw_hat_df 有 head 方法

物件導向2
一個物件（Object）屬於一種類別（Class）
定義類別

class Strawhat:

  ''' 

  這是一個叫做 Strawhat 的類別 

  ''' 

  def __init__(self, name, age): 

    self.name = name 

    self.age = age 

# 根據 Strawhat 類別建立兩個物件 

luffy = Strawhat("蒙其·D·魯夫", 19) 

zoro = Strawhat("羅羅亞·索隆", 21) 

# 使用屬性 

print(luffy.name) 

print(luffy.age) 

print(luffy.__doc__) 

print(zoro.name) 

print(zoro.age) 

print(zoro.__doc__)

物件導向3
定義類別的方法

class Strawhat: 

  ''' 這是一個叫做 Strawhat 的類別 ''' def __init__(self, name, age): self.name = name self.age = age def print_age_2_yr_ago(self): age_2_yr_age = self.age - 2 print(self.name, "兩年前是", age_2_yr_age, "歲。") # 根據 Strawhat 類別建立物件 luffy = Strawhat("蒙其·D·魯夫", 19) # 使用 luffy 的 print_age_2_yr_ago() 方法 luffy.print_age_2_yr_ago()

文字(str)

my_str = "python for Data science!!!"

print(my_str.startswith("Python"))

print(my_str.endswith("!"))

print(my_str.find("for"))

print(my_str.count("!"))

print(my_str.capitalize())

print(my_str.title())

print(my_str.upper())

print(my_str.lower())

print(my_str.swapcase())

print(my_str.replace("science", "analysis"))

out:

False

True 

7 

3 

Python for data science!!! 

Python For Data Science!!! 

PYTHON FOR DATA SCIENCE!!! 

python for data science!!! 

PYTHON FOR dATA SCIENCE!!! 

python for Data analysis!!!

list

age = [19, 21, 20, 19, 21, 17, 30, 36]

age.append(90)

print(age)

brook_age = age.pop()

print(brook_age)

print(age)

age.insert(8, brook_age)

age.remove(brook_age)

print(age)

print(age.index(21))

age.append(90)

age.append(90)

print(age.count(90))

age.sort()

print(age)

age.reverse()

print(age)

out:
[19, 21, 20, 19, 21, 17, 30, 36, 90]
90 

[19, 21, 20, 19, 21, 17, 30, 36] 

[19, 21, 20, 19, 21, 17, 30, 36] 

1 

2 

[17, 19, 19, 20, 21, 21, 30, 36, 90, 90] 

[90, 90, 36, 30, 21, 21, 20, 19, 19, 17]

dict

# as of 2016-12-13

team_name = "金州勇士隊"

wins = 21

losses = 4

win_percentage = 0.84

is_on_winning_streak = True

golden_state_warriors = {

    "team_name": team_name,

    "wins": wins,

    "losses": losses,

    "win_percentage": win_percentage,

    "is_on_winning_streak": is_on_winning_streak

}

print(golden_state_warriors.get("wins"))

print(golden_state_warriors.keys()) # 用 list 把結果轉換

print(golden_state_warriors.values()) # 用 list 把結果轉換

out:

21

dict_keys(['is_on_winning_streak', 'wins', 'team_name', 'win_percentage', 'losses']) dict_values([True, 21, '金州勇士隊', 0.84, 4])

ndarray

import numpy as np

print(np.zeros(6)) # 六個元素均為零的 1d array

print(np.arange(11)) # 十一個元素為 0 到 10 的 1d array

print(np.arange(1, 11)) # 十個元素為 1 到 10 的 1d array

print(np.arange(10).astype(np.str)) # 利用 astype() 方法轉換為文字 

print(np.arange(10).reshape(2, 5)) # 利用 reshape() 轉換為矩陣

print(np.arange(10).reshape(2, 5).T) # 利用 T 屬性轉置

print(np.random.normal(size = 10)) # 生成 10 組標準常態分配（平均值為 0，標準差為 1 的常態分配）隨機變數

print(np.random.uniform(size = 10)) # 生成 10 組介於 0 與 1 之間均勻分配隨機變數

out:

[ 0. 0. 0. 0. 0. 0.]

[ 0 1 2 3 4 5 6 7 8 9 10] 

[ 1 2 3 4 5 6 7 8 9 10] 

['0' '1' '2' '3' '4' '5' '6' '7' '8' '9'] 

[[0 1 2 3 4] [5 6 7 8 9]] 

[[0 5] [1 6] [2 7] [3 8] [4 9]] 

[-1.54011452 0.01713279 -0.03278685 1.4184904 1.75206569 -1.14644167 1.912005 -0.41178504 0.11401443 1.28097786] 

[ 0.12950068 0.55311978 0.18856714 0.59106765 0.21379646 0.010011 0.88442601 0.11328004 0.32626053 0.46299239]

data frame

import pandas as pd

name = ["蒙其·D·魯夫", "羅羅亞·索隆", "娜美", "騙人布", "賓什莫克·香吉士", "多尼多尼·喬巴", "妮可·羅賓", "佛朗基", "布魯克"]

age = [19, 21, 20, 19, 21, 17, 30, 36, 90]

# 建立 dict

straw_hat_dict = {"name": name,

                  "age": age

}

# 建立 data frame

straw_hat_df = pd.DataFrame(straw_hat_dict)

# 刪除觀測值或欄位

straw_hat_df.drop(0, axis = 0)

straw_hat_df.drop(1, axis = 1)

# 選擇觀測值或欄位

straw_hat_df.ix[0, :]

straw_hat_df.ix[:, 0]

straw_hat_df.ix[0, 0]

# 透過布林值篩選

filter = straw_hat_df.age >= 30

straw_hat_df[filter]

filter = straw_hat_df.age < 30

filter_2 = straw_hat_df.name == "蒙其·D·魯夫"

straw_hat_df[filter & filter_2]

查詢
在 Jupyter Notebook 中以 tab 鍵查詢
以 dir() 函數查詢=> $ dit()
官方文件與 Google