【Python】Python_learning3:Windows7/10 - Pycharm下安装使用python科学计算库

本文介绍了如何在Windows系统中使用PyCharm安装和配置Python科学计算库,如Anaconda、NumPy、Pandas、SciPy和Matplotlib。通过Anaconda获取Python环境和科学计算模块,同时提供了PyCharm的配置指导。

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Python Introduction

  • Python是一种强大的编程语言,其提供了很多用于科学计算的模块,常见的包括numpy、scipy、pandas和matplotlib:如下所言:
  • Anaconda 4.1.1 includes an easy installation of Python (2.7.12, 3.4.5, and/or 3.5.2) and updates of over 100 pre-built and tested scientific and analytic Python packages that include NumPy, Pandas, SciPy, Matplotlib, and IPython, with over 620 more packages available via a simple “conda install <packagename>”.
  • 要利用Python进行科学计算,就需要一一安装所需的模块,而这些模块可能又依赖于其它的软件包或库。
  • Anaconda includes several open source development environments such as Jupyter/IPython and Spyder and is supported by Sublime Text 2 and PyCharm. Packages are regularly added. Anaconda is available for Linux, OS X and Windows, and is always proudly free and Open Source.
  • Anaconda就是其中一个常用的科学计算发行版。

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资源下载

D:\PyCharm\stock_predict_with_LSTM-master\venv\Scripts\python.exe D:/PyCharm/stock_predict_with_LSTM-master/main.py 2025-06-10 23:48:11.036396: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library &#39;cudart64_110.dll&#39;; dlerror: cudart64_110.dll not found 2025-06-10 23:48:11.036609: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. [ 2025/06/10 23:48:13 ] Config: &#39;add_train&#39;: False &#39;batch_size&#39;: 64 &#39;continue_flag&#39;: &#39;&#39; &#39;cur_time&#39;: &#39;2025_06_10_23_48_13&#39; &#39;debug_mode&#39;: False &#39;debug_num&#39;: 500 &#39;do_continue_train&#39;: False &#39;do_figure_save&#39;: False &#39;do_log_print_to_screen&#39;: True &#39;do_log_save_to_file&#39;: True &#39;do_predict&#39;: True &#39;do_train&#39;: True &#39;do_train_visualized&#39;: False &#39;dropout_rate&#39;: 0.2 &#39;epoch&#39;: 20 &#39;feature_columns&#39;: [2, 3, 4, 5, 6, 7, 8] &#39;figure_save_path&#39;: &#39;./figure/&#39; &#39;hidden_size&#39;: 128 &#39;input_size&#39;: 7 &#39;label_columns&#39;: [4, 5] &#39;label_in_feature_index&#39;: [2, 3] &#39;learning_rate&#39;: 0.001 &#39;log_save_path&#39;: &#39;./log/2025_06_10_23_48_13_tensorflow/&#39; &#39;lstm_layers&#39;: 2 &#39;model_name&#39;: &#39;model_tensorflow.h5&#39; &#39;model_postfix&#39;: {&#39;pytorch&#39;: &#39;.pth&#39; &#39;keras&#39;: &#39;.h5&#39; &#39;tensorflow&#39;: &#39;.h5&#39;} &#39;model_save_path&#39;: &#39;./checkpoint/tensorflow/&#39; &#39;output_size&#39;: 2 &#39;patience&#39;: 5 &#39;predict_day&#39;: 1 &#39;random_seed&#39;: 42 &#39;shuffle_train_data&#39;: True &#39;time_step&#39;: 20 &#39;train_data_path&#39;: &#39;./data/stock_data.csv&#39; &#39;train_data_rate&#39;: 0.95 &#39;use_cuda&#39;: False &#39;used_frame&#39;: &#39;tensorflow&#39; &#39;valid_data_rate&#39;: 0.15 2025-06-10 23:48:13.116524: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library &#39;nvcuda.dll&#39;; dlerror: nvcuda.dll not found 2025-06-10 23:48:13.116622: W tensorflow/stream_executor/cuda/cuda_driver.cc:263] failed call to cuInit: UNKNOWN ERROR (303) 2025-06-10 23:48:13.119797: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: LAPTOP-VTR0417G 2025-06-10 23:48:13.119968: I tensorflow/stream_executor/cuda/cuda_diagnost
最新发布
06-11
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