ffmpeg官網,這是一款用指令達成的影片工具
# ubuntu
sudo apt-get install ffmpeg| 1.116.136.219 | |
| 1.117.174.124 | |
| 1.14.109.65 | |
| 1.14.44.105 | |
| 1.15.247.236 | |
| 1.164.105.71 | |
| 1.164.108.115 | |
| 1.164.115.116 | |
| 1.164.120.129 | |
| 1.164.120.61 |
| """ | |
| Author: owo | |
| Date: 2023/09/09 | |
| Post: https://blog.o-w-o.cc/archives/streamlit-chatelements | |
| License: CC BY-NC-SA 4.0 | |
| """ | |
| import streamlit as st | |
| import numpy as np | |
| MODE = st.sidebar.selectbox("選擇展示模式", ['chat_input用法', 'chat_message用法', '組合', '組合(含紀錄)']) |
| # -*- coding: utf-8 -*- | |
| """DAC HW1_劉弘祥 | |
| Automatically generated by Colaboratory. | |
| Original file is located at | |
| https://colab.research.google.com/drive/1dwcMqxWb29IuwAxoX_6xn-ct64VHOLFD | |
| # DAC-Python HW1 | |
| + 時間:2023/03/01 |
| { | |
| "model": "text-davinci-003", | |
| "prompt": "{{1.events[].message.text}}", | |
| "max_tokens": 100 | |
| } |
| #%% 讀取資料 | |
| import pandas as pd | |
| orders_df = pd.read_excel('Order1.xlsx') | |
| orders_df.columns | |
| #%% 合併訂單產品ID | |
| orders_df["TradeProductID"] = orders_df["TradesID"] + orders_df["ProductID"] | |
| #%% 篩選return |
| import sys | |
| print(sys.executable) # 顯示目前python解釋器的路徑 | |
| print(help("modules")) # 顯示所有目前的模組 |
| # %% | |
| from opencc import OpenCC | |
| mode_config = { | |
| "簡體->繁體":"s2t", | |
| "簡體->繁體台灣":"s2tw", | |
| "簡體->繁體台灣(含慣用詞)": "s2twp" | |
| } | |
| MODE = "簡體->繁體台灣(含慣用詞)" | |
| FILE_INPUT = "data/input.txt" |
ffmpeg官網,這是一款用指令達成的影片工具
# ubuntu
sudo apt-get install ffmpeg| from sklearn.ensemble import RandomForestClassifier | |
| import matplotlib.pyplot as plt | |
| feature_labels = list(train_X.columns) # 欄位名稱存下來等下顯示用 | |
| forest = RandomForestClassifier().fit(train_X, train_Y) # 送進模型fit | |
| # 用`.feature_importances_`取得重要性 | |
| importances = forest.feature_importances_ | |
| # 取得對應的index,等下顯示用 | |
| indices = np.argsort(importances)[::-1] |
| # 統計敘述(個別) | |
| df.max() | |
| df.mean() | |
| df.count() # 回傳非nan數量 | |
| # 統計敘述(統合) | |
| df.describe() | |
| # 唯一值 | |
| df.apply(lambda x:x.unique(),axis=0) # uniques by columns |