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@@ -12,12 +12,12 @@
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"translatedContent": "<pre>\nDataFrame <code>products</code>\n+-------------+--------+\n| Column Name | Type |\n+-------------+--------+\n| name | object |\n| quantity | int |\n| price | int |\n+-------------+--------+\n</pre>\n\n<p>编写一个解决方案,在 <code>quantity</code> 列中将缺失的值填充为 <code><strong>0</strong></code>。</p>\n\n<p>返回结果如下示例所示。</p>\n\n<p> </p>\n<strong class=\"example\">示例 1:</strong>\n\n<pre>\n<strong>输入:\n</strong>+-----------------+----------+-------+\n| name | quantity | price |\n+-----------------+----------+-------+\n| Wristwatch | 32 | 135 |\n| WirelessEarbuds | None | 821 |\n| GolfClubs | None | 9319 |\n| Printer | 849 | 3051 |\n+-----------------+----------+-------+\n<strong>输出:\n</strong>+-----------------+----------+-------+\n| name | quantity | price |\n+-----------------+----------+-------+\n| Wristwatch | 32 | 135 |\n| WirelessEarbuds | 0 | 821 |\n| GolfClubs | 0 | 9319 |\n| Printer | 849 | 3051 |\n+-----------------+----------+-------+\n<b>解释:</b>\nToaster 和 Headphones 的数量被填充为 0。</pre>\n",
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"difficulty": "Easy",
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@@ -28,7 +28,7 @@
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"hints": [
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"Consider using a build-in function in pandas library to fill the missing values of specified columns."
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