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| TensorFlow를 브라우저에서 실행시키는 공식 프로젝트입니다. (0) | 2018.04.04 |
데이터 분석을 위한 필수 패키지 삼대장이 있습니다. 바로 Pandas와 Numpy 그리고 Matplotlib입니다.
CMD는 "관리자 권한으로 실행" 하시오.
판다스(Pandas)는 파이썬 데이터 처리를 위한 라이브러리입니다. 파이썬을 이용한 데이터 분석과 같은 작업에서 필수 라이브러리로 알려져있습니다. 참고 할 수 있는 Pandas 링크는 다음과 같습니다.
링크 : http://pandas.pydata.org/pandas-docs/stable/
아나콘다를 설치하지 않았다면 아래의 커맨드로 Pandas를 별도 설치할 수 있습니다.
pip install pandas
pip uninstall pandas
>import pandas as pd
>pd.__version__
Pandas는 총 세 가지의 데이터 구조를 사용합니다.
1. 시리즈(Series)
2. 데이터프레임(DataFrame)
3. 패널(Panel)
1. 시리즈(Series)
sr = pd.Series([17000, 18000, 1000, 5000],
index=["피자", "치킨", "콜라", "맥주"])
print(sr)
피자 17000
치킨 18000
콜라 1000
맥주 5000
dtype: int64
print(sr.values)
[17000 18000 1000 5000]
print(sr.index)
Index(['피자', '치킨', '콜라', '맥주'], dtype='object')데이터프레임은 2차원 리스트를 매개변수로 전달합니다. 2차원이므로 행방향 인덱스(index)와 열방향 인덱스(column)가 존재합니다. 즉, 행과 열을 가지는 자료구조입니다. 시리즈가 인덱스(index)와 값(values)으로 구성된다면, 데이터프레임은 열(columns)까지 추가되어 열(columns), 인덱스(index), 값(values)으로 구성됩니다.
values = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
index = ['one', 'two', 'three']
columns = ['A', 'B', 'C']
df = pd.DataFrame(values, index=index, columns=columns)
print(df)
A B C
one 1 2 3
two 4 5 6
three 7 8 9print(df.index) # 인덱스 출력
Index(['one', 'two', 'three'], dtype='object')
print(df.columns) # 열 출력
Index(['A', 'B', 'C'], dtype='object')
print(df.values) # 값 출력
[[1 2 3]
[4 5 6]
[7 8 9]]
넘파이(Numpy)는 수치 데이터를 다루는 파이썬 패키지입니다. Numpy의 핵심이라고 불리는 다차원 행렬 자료구조인 ndarray를 통해 벡터 및 행렬을 사용하는 선형 대수 계산에서 주로 사용됩니다. Numpy는 편의성뿐만 아니라, 속도면에서도 순수 파이썬에 비해 압도적으로 빠르다는 장점이 있습니다.
pip install numpy
> ipython
...
In [1]: import numpy as np
In [2]: np.__version__
Out[2]: '1.16.5'Numpy의 주요 모듈은 아래와 같습니다.
1. np.array() # 리스트, 튜플, 배열로 부터 ndarray를 생성
2. np.asarray() # 기존의 array로 부터 ndarray를 생성
3. np.arange() # range와 비슷
4. np.linspace(start, end, num) # [start, end] 균일한 간격으로 num개 생성
5. np.logspace(start, end, num) # [start, end] log scale 간격으로 num개 생성
맷플롯립(Matplotlib)은 데이터를 차트(chart)나 플롯(plot)으로 시각화(visulaization)하는 패키지입니다. 데이터 분석에서 Matplotlib은 데이터 분석 이전에 데이터 이해를 위한 시각화나, 데이터 분석 후에 결과를 시각화하기 위해서 사용됩니다.
%matplotlib inline
import matplotlib.pyplot as plt
plt.title('test')
plt.plot([1,2,3,4],[2,4,8,6])
plt.show()
위키독스
온라인 책을 제작 공유하는 플랫폼 서비스
wikidocs.net
| [python] pandas 외부csv 파일 읽기 (0) | 2020.12.01 |
|---|---|
| [python] Online python Compiler , 온라인 파이썬 컴파일러 (0) | 2020.11.30 |
| [python] pandas 설치 후 import 했는데 오류날때 (0) | 2020.11.26 |
| [python] Google Cloud Speech - StreamingRecognitionResult (0) | 2020.11.13 |
| [python] Package google.cloud.speech.v1 (0) | 2020.11.13 |
pandas 설치 후 import 했는데 오류날때
fails to pass a sanity check due to a bug in the windows runtime. See this issue for more information: https://tinyurl.com/y3dm3h86RuntimeError: The current Numpy installation fails to pass a sanity check due to a bug in the windows runtime
I am using Python 3.9 on Windows 10 version 2004 x64. PowerShell as Administrator. Python verion: Python 3.9.0 (tags/v3.9.0:9cf6752, Oct 5 2020, 15:34:40) [MSC v.1927 64 bit (AMD64)] on win32 Ins...
stackoverflow.com
pip install virtualenv
virtualenv foo
cd .\foo
.\Scripts\active
pip install numpy
pip install matplotlib
| [python] Online python Compiler , 온라인 파이썬 컴파일러 (0) | 2020.11.30 |
|---|---|
| python, pandas, numpy, Matplotlib (0) | 2020.11.27 |
| [python] Google Cloud Speech - StreamingRecognitionResult (0) | 2020.11.13 |
| [python] Package google.cloud.speech.v1 (0) | 2020.11.13 |
| [python] 현재 시간, 일시 (0) | 2020.11.13 |
A streaming speech recognition result corresponding to a portion of the audio that is currently being processed.
Fields
#alternatives
#channel_tag
| alternatives[] |
May contain one or more recognition hypotheses (up to the maximum specified in max_alternatives). These alternatives are ordered in terms of accuracy, with the top (first) alternative being the most probable, as ranked by the recognizer. |
| is_final |
bool If false, this StreamingRecognitionResult represents an interim result that may change. If true, this is the final time the speech service will return this particular StreamingRecognitionResult, the recognizer will not return any further hypotheses for this portion of the transcript and corresponding audio. |
| stability |
float An estimate of the likelihood that the recognizer will not change its guess about this interim result. Values range from 0.0 (completely unstable) to 1.0 (completely stable). This field is only provided for interim results (is_final=false). The default of 0.0 is a sentinel value indicating stability was not set. |
| result_end_time |
Time offset of the end of this result relative to the beginning of the audio. |
| channel_tag |
int32 For multi-channel audio, this is the channel number corresponding to the recognized result for the audio from that channel. For audio_channel_count = N, its output values can range from '1' to 'N'. |
Package google.cloud.speech.v1 | Cloud Speech-to-Text 문서
phrases[] string A list of strings containing words and phrases "hints" so that the speech recognition is more likely to recognize them. This can be used to improve the accuracy for specific words and phrases, for example, if specific commands are typicall
cloud.google.com
| python, pandas, numpy, Matplotlib (0) | 2020.11.27 |
|---|---|
| [python] pandas 설치 후 import 했는데 오류날때 (0) | 2020.11.26 |
| [python] Package google.cloud.speech.v1 (0) | 2020.11.13 |
| [python] 현재 시간, 일시 (0) | 2020.11.13 |
| [google cloud] 여러 채널로 오디오 스크립트 작성 - transcribe_multichannel.py (0) | 2020.11.13 |
Provides information to the recognizer that specifies how to process the request.
Fields
| encoding |
Encoding of audio data sent in all RecognitionAudio messages. This field is optional for FLAC and WAV audio files and required for all other audio formats. For details, see AudioEncoding. |
| sample_rate_hertz |
int32 Sample rate in Hertz of the audio data sent in all RecognitionAudio messages. Valid values are: 8000-48000. 16000 is optimal. For best results, set the sampling rate of the audio source to 16000 Hz. If that's not possible, use the native sample rate of the audio source (instead of re-sampling). This field is optional for FLAC and WAV audio files, but is required for all other audio formats. For details, see AudioEncoding. |
| audio_channel_count |
int32 The number of channels in the input audio data. ONLY set this for MULTI-CHANNEL recognition. Valid values for LINEAR16 and FLAC are 1-8. Valid values for OGG_OPUS are '1'-'254'. Valid value for MULAW, AMR, AMR_WB and SPEEX_WITH_HEADER_BYTE is only 1. If 0 or omitted, defaults to one channel (mono). Note: We only recognize the first channel by default. To perform independent recognition on each channel set enable_separate_recognition_per_channel to 'true'. |
| enable_separate_recognition_per_channel |
bool This needs to be set to true explicitly and audio_channel_count > 1 to get each channel recognized separately. The recognition result will contain a channel_tag field to state which channel that result belongs to. If this is not true, we will only recognize the first channel. The request is billed cumulatively for all channels recognized: audio_channel_count multiplied by the length of the audio. |
| language_code |
string Required. The language of the supplied audio as a BCP-47 language tag. Example: "en-US". See Language Support for a list of the currently supported language codes. |
| max_alternatives |
int32 Maximum number of recognition hypotheses to be returned. Specifically, the maximum number of SpeechRecognitionAlternative messages within each SpeechRecognitionResult. The server may return fewer than max_alternatives. Valid values are 0-30. A value of 0 or 1 will return a maximum of one. If omitted, will return a maximum of one. |
| profanity_filter |
bool If set to true, the server will attempt to filter out profanities, replacing all but the initial character in each filtered word with asterisks, e.g. "f***". If set to false or omitted, profanities won't be filtered out. |
| speech_contexts[] |
Array of SpeechContext. A means to provide context to assist the speech recognition. For more information, see speech adaptation. |
| enable_word_time_offsets |
bool If true, the top result includes a list of words and the start and end time offsets (timestamps) for those words. If false, no word-level time offset information is returned. The default is false. |
| enable_automatic_punctuation |
bool If 'true', adds punctuation to recognition result hypotheses. This feature is only available in select languages. Setting this for requests in other languages has no effect at all. The default 'false' value does not add punctuation to result hypotheses. |
| diarization_config |
Config to enable speaker diarization and set additional parameters to make diarization better suited for your application. Note: When this is enabled, we send all the words from the beginning of the audio for the top alternative in every consecutive STREAMING responses. This is done in order to improve our speaker tags as our models learn to identify the speakers in the conversation over time. For non-streaming requests, the diarization results will be provided only in the top alternative of the FINAL SpeechRecognitionResult. |
| metadata |
Metadata regarding this request. |
| model |
string Which model to select for the given request. Select the model best suited to your domain to get best results. If a model is not explicitly specified, then we auto-select a model based on the parameters in the RecognitionConfig.
|
| use_enhanced |
bool Set to true to use an enhanced model for speech recognition. If use_enhanced is set to true and the model field is not set, then an appropriate enhanced model is chosen if an enhanced model exists for the audio. If use_enhanced is true and an enhanced version of the specified model does not exist, then the speech is recognized using the standard version of the specified model. |
https://cloud.google.com/speech-to-text/docs/reference/rpc/google.cloud.speech.v1#recognitionconfig
Package google.cloud.speech.v1 | Cloud Speech-to-Text 문서
phrases[] string A list of strings containing words and phrases "hints" so that the speech recognition is more likely to recognize them. This can be used to improve the accuracy for specific words and phrases, for example, if specific commands are typicall
cloud.google.com
| [python] pandas 설치 후 import 했는데 오류날때 (0) | 2020.11.26 |
|---|---|
| [python] Google Cloud Speech - StreamingRecognitionResult (0) | 2020.11.13 |
| [python] 현재 시간, 일시 (0) | 2020.11.13 |
| [google cloud] 여러 채널로 오디오 스크립트 작성 - transcribe_multichannel.py (0) | 2020.11.13 |
| google.api_core.exceptions.InvalidArgument: 400 Request payload size exceeds the limit: 10485760 bytes. (0) | 2020.11.13 |
[Python] argparse 사용법 (파이썬 인자값 추가하기)
두개의 인자 path, savefilename 을 받음.
(env) C:\__STT>python transcribe_async_gcs.py gs://cloud-samples-tests/speech/vr.flac 12345
Waiting for operation to complete...
Transcript: it's okay so what am I doing here why am I here at GDC talking about VR video it's because I believe my favorite games I love games I believe in games my favorite games are the ones that are all about the stories I love narrative game design I love narrative-based games and I think that when it comes to telling stories in VR bring together capturing the world with narrative based games and narrative based game design is going to unlock some of the killer apps and killer stories of the medium
Confidence: 0.9580045938491821
Transcript: so I'm really here looking for people who are interested in telling us or two stories that are planning projects around telling those types of stories and I would love to talk to you so if it sounds like your project if you're looking at blending VR video and interactivity to tell a story I want to talk to you I want to help you so if this sounds like you please get in touch with you can't find me I'll be here all week I have pink hair I work for Google and I would love to talk with you further about VR video interactivity and storytelling
Confidence: 0.949270486831665
completedif __name__ == "__main__":
parser = argparse.ArgumentParser(
description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
)
parser.add_argument("path", help="File or GCS path for audio file to be recognized")
parser.add_argument("savefilename", help="Save fileName ")
args = parser.parse_args()
response = transcribe_gcs(args.path)
with open("stt_"+args.savefilename+".txt", "w") as script:
for result in response.results:
script.write(u'Transcript: {}'.format(result.alternatives[0].transcript)+"\n")
script.write(u'Confidence: {}'.format(result.alternatives[0].confidence)+"\n")
script.write(u'Channel Tag: {}'.format(result.alternatives[0].channel_tag)+"\n")docs.python.org/ko/3/library/argparse.html
argparse — 명령행 옵션, 인자와 부속 명령을 위한 파서 — Python 3.9.0 문서
argparse — 명령행 옵션, 인자와 부속 명령을 위한 파서 소스 코드: Lib/argparse.py argparse 모듈은 사용자 친화적인 명령행 인터페이스를 쉽게 작성하도록 합니다. 프로그램이 필요한 인자를 정의하면
docs.python.org
docs.python.org/ko/3/howto/argparse.html
Argparse 자습서 — Python 3.9.0 문서
Argparse 자습서 저자 Tshepang Lekhonkhobe 이 자습서는 파이썬 표준 라이브러리에서 권장하는 명령행 파싱 모듈인 argparse 에 대한 소개입니다. 참고 같은 작업을 수행하는 다른 두 모듈이 있습니다, geto
docs.python.org