티스토리 뷰
PyPI
- Python Package Index
- 파이썬 관련 패키지들의 Repository
- https://pypi.org/
# pip를 이용한 설치 방법
# 최신 버전
pip install 'SomeProject'
# 특정 버전
pip install 'SomeProject==1.4'
# 조건
pip install 'SomeProject>=1,<2'
pip install 'SomeProject~=1.4.2'
출처: https://packaging.python.org/tutorials/installing-packages/
주요 패키지
NumPy
Pandas
- Link
- 데이터 분석을 위해 R의 dataframe를 참조하여 만듬.
- Tutorial
- https://www.datacamp.com/community/tutorials/pandas-tutorial-dataframe-python
- Q&A와 함께 단계적으로 설명
- Python - Pandas 튜토리얼 1(데이터프레임 생성, 접근, 삭제, 수정)
- ndarray, dictionary, dataframe, series, list
# Take a 2D array as input to your DataFrame
my_2darray = np.array([[1, 2, 3], [4, 5, 6]])
print(pd.DataFrame(my_2darray))
0 1 2
0 1 2 3
1 4 5 6
# Take a dictionary as input to your DataFrame
my_dict = {1: ['1', '3'], 2: ['1', '2'], 3: ['2', '4']}
print(pd.DataFrame(my_dict))
1 2 3
0 1 1 2
1 3 2 4
# Take a DataFrame as input to your DataFrame
my_df = pd.DataFrame(data=[4,5,6,7], index=range(0,4), columns=['A'])
print(pd.DataFrame(my_df))
A
0 4
1 5
2 6
3 7
# Take a Series as input to your DataFrame
my_series = pd.Series({"United Kingdom":"London", "India":"New Delhi", "United States":"Washington", "Belgium":"Brussels"})
print(pd.DataFrame(my_series))
0
Belgium Brussels
India New Delhi
United Kingdom London
United States Washington
df = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6]]))
# Use the `shape` property
print(df.shape)
(2, 3)
# Or use the `len()` function with the `index` property
print(len(df.index))
2
Matplotlib
- Link
- 2차원 데이터 시각화 라이브러리
IPython
- 데이터 처리 및 시각화에 유용
- 파이썬 쉘 제공(테스트 및 디버깅)
추가 주요 패키지
명 |
내용 |
Python-based ecosystem of open-source software for mathematics, science, and engineering. |
|
SymPy is a Python library for symbolic mathematics. |
|
Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. |
|
Machine Learning in Python
|
|
TensorFlow™ is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. |
|
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. |
Anaconda
'공부하자 > 머신러닝' 카테고리의 다른 글
ML02. AI, Machine Learning, Deep Learning Overview (0) | 2019.03.18 |
---|---|
ML01. 학습 및 참고 사이트 (0) | 2019.03.18 |
Analytics 유형, Types of Data Analytics (0) | 2017.09.11 |
#4. Linear Regression (0) | 2017.09.01 |
#3. Window 기반 Python 개발 환경 (0) | 2017.09.01 |
- Total
- Today
- Yesterday
- Aging
- 가상호스트
- deep learning
- 리눅스
- VirtualHost
- 달인
- pygr
- Next Generation Sequencing
- 우드스토브
- edwith
- 리눅스 모니터링
- Mate pair
- Python
- 생물정보기업
- wget
- color scripter
- illumina
- Bioinformatics
- GaN
- MySQL
- paired-end
- signalP
- transmembrane
- tensorflow
- TM
- CNV
- 화목난로
- xbrowser
- machine learning
- ssh
일 | 월 | 화 | 수 | 목 | 금 | 토 |
---|---|---|---|---|---|---|
1 | 2 | |||||
3 | 4 | 5 | 6 | 7 | 8 | 9 |
10 | 11 | 12 | 13 | 14 | 15 | 16 |
17 | 18 | 19 | 20 | 21 | 22 | 23 |
24 | 25 | 26 | 27 | 28 | 29 | 30 |
31 |