數值資料:程式設計練習
透過集合功能整理內容
你可以依據偏好儲存及分類內容。
請花點時間完成下列練習,練習在「使用數值資料的第一步」一文中學到的內容。
- 取得資料集的統計資料,說明如何找出含有明顯離群值的資料欄:
- 找出資料集的錯誤部分,這項功能會透過視覺和數學方式,協助您找出資料集中隱藏的錯誤值:
您可以使用 Colaboratory 平台,直接在瀏覽器中執行程式設計練習 (無須進行設定)。Colaboratory 支援大多數主流瀏覽器,且在 Chrome 和 Firefox 電腦版上經過最徹底的測試。
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上次更新時間:2025-01-29 (世界標準時間)。
[null,null,["上次更新時間:2025-01-29 (世界標準時間)。"],[[["\u003cp\u003eThis page provides programming exercises focusing on practicing numerical data analysis skills learned in a previous lesson.\u003c/p\u003e\n"],["\u003cp\u003eTwo Colab exercises are available: one on calculating descriptive statistics and identifying outliers, and another on detecting and handling bad data values in a dataset.\u003c/p\u003e\n"],["\u003cp\u003eThe exercises are browser-based and require no setup, utilizing the Colaboratory platform, primarily supported on Chrome and Firefox desktop versions.\u003c/p\u003e\n"]]],[],null,["# Numerical data: Programming exercises\n\nTake some time to complete the following exercises to practice what you've\nlearned in\n[First steps with numerical data](/machine-learning/crash-course/numerical-data/first-steps).\n\n- **Get statistics on a dataset** , which shows you how to find columns containing blatant outliers: \n [Open math statistics exercise](https://colab.research.google.com/github/google/eng-edu/blob/main/ml/cc/exercises/numerical_data_stats.ipynb?utm_source=mlcc&utm_campaign=colab-external&utm_medium=referral&utm_content=numerical_data_stats)\n- **Find the bad part of the dataset** , which guides you through visual and mathematical ways to find hidden *bad* values in a dataset: \n [Open \"bad part\" dataset exercise](https://colab.research.google.com/github/google/eng-edu/blob/main/ml/cc/exercises/numerical_data_bad_values.ipynb?utm_source=mlcc&utm_campaign=colab-external&utm_medium=referral&utm_content=numerical_data_bad_values)\n\nProgramming exercises run directly in your browser (no setup\nrequired!) using the [Colaboratory](https://colab.research.google.com)\nplatform. Colaboratory is supported on most major browsers, and is most\nthoroughly tested on desktop versions of Chrome and Firefox. \n[Help Center](https://support.google.com/machinelearningeducation)"]]