【Python 資料科學教程】環境架設、Numpy, Pandas 基礎 — Data Science with Python

JiunYi Yang (JY)

Published in

資料探員 Data Agent

4 min readFeb 11, 2021

2021 年 2 月 11 日 Python, tutorial

教程大綱

Explortary / Statistical Data Analysis
Feature Engineering — Feature Selection
Machine Learning Model Training

Supervised Learning
Classification
KNN
Regression
Unsupervised Learning
Clustering
Association Rule Learning

Deep Learning Model Training

Time Series Data
LSTM
GRU
Natural Language Processing (NLP)
Image Recognition
Other

1. Environment Setup

開始寫 Python 程式進行資料處理之前，我們要先做好環境架設：

Windows

安裝 Anaconda 及 Python:

下載 Anaconda installer
打開 “Anaconda Prompt“
輸入 conda list 來確認是否安裝成功
輸入 python 來確認你目前的 Python 版本（輸入 quit() 可以跳出 Python shell）

安裝完成後，我們要先建立虛擬環境，再來安裝需要的 package

打開 “Anaconda Prompt”
創建虛擬環境：
輸入conda create --name env_name python=3.7 anaconda

anaconda: 這個指令是為了讓創建的虛擬環境自動納入 anaconda 預設的 packages

啟動虛擬環境：
conda activate env_name

當你看到前面換成 (env_name) 時，便成功啟動了

2. Data Processing

Numpy Basics

可對陣列進行數學或邏輯運算
線性代數運算
產生隨機亂數等

Numpy 1D Arrays 一維陣列

1. broadcast operations：對 ndarray 可以進行 broadcast 數學運算，對 list 則無法

example_list = [45, 69, 94, 40, 694, 596, 504]
example_array = np.array(example_list)

2. condition selection：對 ndarray 可以進行邏輯篩選，對 list 則無法

filter = example_list > 50

Numpy 2D Arrays 二維陣列

3. slicing：對 ndarray 可以進行切片選取範圍，對 list 則無法

example_list = [[5,6,7,8,9], 
                [7,8,9,10,11],
                [9,10,11,12,13]]
example_list[:, 1:4]
example_array[:, 1:4]

4. array operations：對 ndarray 可以進行陣列相乘，list 則無法

multiplier = [34, 78, 90, 5, 9]
example_list * multiplier
example_array * np.array(multiplier)

5. basic statistics functions

Average: np.mean()
Median: np.median()
Standard derivation: np.std()
Pearson’s correlation: np.corrcoef()

In 1D Array:

def func(x, axis):
    print(np.mean(example_list, axis=axis))
    print(np.median(example_list, axis=axis))
    print(np.std(example_list, axis=axis))
    print(np.corrcoef(example_list), '\n')

In 2D Array:

axis=0: verticality operation
axis=1: horizontally operation

example_array = np.array(example_list)
print('verticality: ')
func(example_array, axis=0)
print('horizontally: ')
func(example_array, axis=1)

Pandas Basics

2 main data structures:

Series
DataFrame

Functionalities:

slicing, indexing and subsetting
groupby
reshape
pivot_table
merge, join, concat, etc.

Series

create series

# create from ndarray
data = np.random.randn(5)
pd.Series(data, index=['a','b','c','d','e'])

# create from dictionary
data = {
    'Facebook': 'Mark Zuckerberg',
    'Apple': 'Steve Jobs',
    'Amazon': 'Jeff Bezos',
    'Netflix': 'Reed Hastings',
    'Google': 'Larry Page'
}
pd.Series(data)

2. demonstrate operations: array-like operations

data = np.random.randn(5)
ser = pd.Series(data)
ser[ser > ser.mean()]

3. demonstrate operations: dict-like operations

ser
ser['a']

DataFrame

create series

# create from dict of seriesdata = {
    'one': pd.Series(np.random.randn(5), index=['a','b','c','d','e']),
    'two': pd.Series(np.random.randn(5), index=['a','b','c','d','e']),
}
df = pd.DataFrame(data)
df

# create from dict of ndarrays/listsdata = {
    'one': np.random.randn(5),
    'two': np.random.randn(5)
}
df = pd.DataFrame(data)
df

# create from list of dictsdata = [{
    'a': 1,
    'b': 2
}, {
    'a': 3,
    'b': 4,
    'c': 6
}]
df = pd.DataFrame(data)
df

# create a MultiIndexed dataframe from tuples dictdata = {
    ('top-1','medium-1'): {('I','i'): 1, ('I','ii'): 2},
    ('top-1','medium-2'): {('I','ii'): 1, ('I','iii'): 2},
    ('top-2','medium-1'): {('I','i'): 1, ('I','ii'): 2},
    ('top-2','medium-2'): {('I','i'): 1, ('I','iii'): 2}
}
df = pd.DataFrame(data)
df

Colab 範本

1. Environment Setup
2–1. Numpy Basics
2–2. Pandas Basics

線上支持這個教程

掃QR碼打賞
至粉專按讚並分享

後續還會繼續更新 Python 資料科學的更多教程，喜歡的同學別忘了按讚打開通知～

- 如果你覺得稍有收穫，請不吝嗇幫我鼓掌，給我持續更新的動力！- 5-10拍手：到此一遊
- 10-40拍手：對文章內容的肯定
- 50拍手：你真的很期待再看到『同類別的』文章，我們會根據拍手數做主題的參考- 粉絲頁：行銷數據分析 Marketing Analytics，歡迎大家 Follow 獲取數位廣告&資料分析知識＆技能！