From 9cf7167ef339aea976395d00433bc4e2a01ee0af Mon Sep 17 00:00:00 2001 From: lolipopshock Date: Wed, 20 Jun 2018 19:43:12 +0800 Subject: [PATCH] Update2.0-1 --- .../Explore Movie Dataset.ipynb | 290 +++++++++++++++++- 1 file changed, 275 insertions(+), 15 deletions(-) diff --git a/P2_Explore_Movie_Dataset/Explore Movie Dataset.ipynb b/P2_Explore_Movie_Dataset/Explore Movie Dataset.ipynb index 9e576ec..55a5fe1 100644 --- a/P2_Explore_Movie_Dataset/Explore Movie Dataset.ipynb +++ b/P2_Explore_Movie_Dataset/Explore Movie Dataset.ipynb @@ -22,18 +22,41 @@ "collapsed": true }, "source": [ - "## 第一节 读取库、导入数据\n", + "## 第一节 数据的导入与处理\n", "\n", - "在这一部分，你需要编写代码，完成以下任务：\n", + "在这一部分，你需要编写代码，使用 Pandas 读取数据，并进行预处理。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "**任务1.1：** 导入库以及数据\n", "\n", "1. 载入需要的库 `NumPy`、`Pandas`、`matplotlib`、`seaborn`。\n", "2. 利用 `Pandas` 库，读取 `tmdb-movies.csv` 中的数据，保存为 `movie_data`。\n", - "3. 使用 `.head()` 方法，来获取数据的前几条数据。\n", - "4. 根据获取的数据，提出两个问题，作为接下来探索数据的目标。\n", "\n", - "提示：\n", - "1. 记得使用 notebook 中的魔法指令 `%matplotlib inline`，否则会导致你接下来无法打印出图像。\n", - "2. 提出的问题应当和数据中的**某个**特征息息相关，例如：大部分电影的票房（revenue）是怎样分布的、大部分电影的知名度（popularity）是怎样分布的。" + "提示：记得使用 notebook 中的魔法指令 `%matplotlib inline`，否则会导致你接下来无法打印出图像。" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('tmdb-movies.csv')" ] }, { @@ -42,7 +65,16 @@ "source": [ "---\n", "\n", - "**任务1：**按照要求完成代码。" + "**任务1.2: ** 了解数据\n", + "\n", + "你会接触到各种各样的数据表，因此在读取之后，我们有必要通过一些简单的方法，来了解我们数据表是什么样子的。\n", + "\n", + "1. 获取数据表的行列，并打印。\n", + "2. 使用 `.head()`、`.tail()`、`.sample()` 方法，观察、了解数据表的情况。\n", + "3. 使用 `.dtypes` 属性，来查看各列数据的数据类型。\n", + "4. 使用 `isnull()` 配合 `.any()` 等方法，来查看各列是否存在空值。\n", + "5. 使用 `.describe()` 方法，看看数据表中数值型的数据是怎么分布的。\n", + "\n" ] }, { @@ -58,17 +90,32 @@ "source": [ "---\n", "\n", - "**任务2：**根据上述数据，提出两个问题，作为接下来探索数据的目标。" + "**任务1.3: ** 清理数据\n", + "\n", + "在真实的工作场景中，数据处理往往是最为费时费力的环节。但是幸运的是，我们提供给大家的 tmdb 数据集非常的「干净」，不需要大家做特别多的数据清洗以及处理工作。在这一步中，你的核心的工作主要是对数据表中的空值进行处理。你可以使用 `.fillna()` 来填补空值，当然也可以使用 `.dropna()` 来丢弃数据表中包含空值的某些行或者列。\n", + "\n", + "任务：使用适当的方法来清理空值，并将得到的数据保存。" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, { "cell_type": "markdown", "metadata": {}, "source": [ + "3. 使用 `.head()` 方法，来获取数据的前几条数据。\n", + "4. 根据获取的数据，提出两个问题，作为接下来探索数据的目标。\n", "\n", - "- 问题 1：（回答区）\n", + "提示：\n", + "1. 记得使用 notebook 中的魔法指令 `%matplotlib inline`，否则会导致你接下来无法打印出图像。\n", + "2. 提出的问题应当和数据中的**某个**特征息息相关，例如：大部分电影的票房（revenue）是怎样分布的、大部分电影的知名度（popularity）是怎样分布的。\n", "\n", - "- 问题 2：（回答区）" + "---\n", + "\n", + "**任务2：**根据上述数据，提出两个问题，作为接下来探索数据的目标。" ] }, { @@ -79,18 +126,218 @@ "\n", "---\n", "\n", - "## 第二节 获取数据的统计信息\n", + "## 第二节 根据指定要求读取数据\n", + "\n", "\n", - "读取数据之后，我们需要获取数据的一些统计信息，例如最大值、最小值、平均数、中位数等。" + "相比 Excel 等数据分析软件，Pandas 的一大特长在于，能够轻松地基于复杂的逻辑选择合适的数据。因此，如何根据指定的要求，从数据表当获取适当的数据，是使用 Pandas 中非常重要的技能，也是本节重点考察大家的内容。\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNotImplementedError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m~/anaconda/envs/robot-env/lib/python3.6/site-packages/pandas/core/groupby.py\u001b[0m in \u001b[0;36m_cython_agg_blocks\u001b[0;34m(self, how, alt, numeric_only, min_count)\u001b[0m\n\u001b[1;32m 3652\u001b[0m result, _ = self.grouper.aggregate(\n\u001b[0;32m-> 3653\u001b[0;31m block.values, how, axis=agg_axis, min_count=min_count)\n\u001b[0m\u001b[1;32m 3654\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda/envs/robot-env/lib/python3.6/site-packages/pandas/core/groupby.py\u001b[0m in \u001b[0;36maggregate\u001b[0;34m(self, values, how, axis, min_count)\u001b[0m\n\u001b[1;32m 2313\u001b[0m return self._cython_operation('aggregate', values, how, axis,\n\u001b[0;32m-> 2314\u001b[0;31m min_count=min_count)\n\u001b[0m\u001b[1;32m 2315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda/envs/robot-env/lib/python3.6/site-packages/pandas/core/groupby.py\u001b[0m in \u001b[0;36m_cython_operation\u001b[0;34m(self, kind, values, how, axis, min_count)\u001b[0m\n\u001b[1;32m 2247\u001b[0m func, dtype_str = self._get_cython_function(\n\u001b[0;32m-> 2248\u001b[0;31m kind, how, values, is_numeric)\n\u001b[0m\u001b[1;32m 2249\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda/envs/robot-env/lib/python3.6/site-packages/pandas/core/groupby.py\u001b[0m in \u001b[0;36m_get_cython_function\u001b[0;34m(self, kind, how, values, is_numeric)\u001b[0m\n\u001b[1;32m 2181\u001b[0m \u001b[0;34m\"dtype: [how->%s,dtype->%s]\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2182\u001b[0;31m (how, dtype_str))\n\u001b[0m\u001b[1;32m 2183\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype_str\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNotImplementedError\u001b[0m: function is not implemented for thisdtype: [how->max,dtype->object]", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m~/anaconda/envs/robot-env/lib/python3.6/site-packages/pandas/core/groupby.py\u001b[0m in \u001b[0;36maggregate\u001b[0;34m(self, arg, *args, **kwargs)\u001b[0m\n\u001b[1;32m 3738\u001b[0m result = self._aggregate_multiple_funcs(\n\u001b[0;32m-> 3739\u001b[0;31m [arg], _level=_level, _axis=self.axis)\n\u001b[0m\u001b[1;32m 3740\u001b[0m result.columns = Index(\n", + "\u001b[0;32m~/anaconda/envs/robot-env/lib/python3.6/site-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m_aggregate_multiple_funcs\u001b[0;34m(self, arg, _level, _axis)\u001b[0m\n\u001b[1;32m 583\u001b[0m \u001b[0mcolg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gotitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 584\u001b[0;31m 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"\u001b[0;31mKeyboardInterrupt\u001b[0m: ", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m~/anaconda/envs/robot-env/lib/python3.6/site-packages/pandas/core/groupby.py\u001b[0m in \u001b[0;36mf\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 1243\u001b[0m return self._cython_agg_general(\n\u001b[0;32m-> 1244\u001b[0;31m alias, alt=npfunc, **kwargs)\n\u001b[0m\u001b[1;32m 1245\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mAssertionError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda/envs/robot-env/lib/python3.6/site-packages/pandas/core/groupby.py\u001b[0m in \u001b[0;36m_cython_agg_general\u001b[0;34m(self, how, alt, numeric_only, min_count)\u001b[0m\n\u001b[1;32m 3608\u001b[0m new_items, new_blocks = self._cython_agg_blocks(\n\u001b[0;32m-> 3609\u001b[0;31m how, alt=alt, numeric_only=numeric_only, min_count=min_count)\n\u001b[0m\u001b[1;32m 3610\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wrap_agged_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_items\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda/envs/robot-env/lib/python3.6/site-packages/pandas/core/groupby.py\u001b[0m in \u001b[0;36m_cython_agg_blocks\u001b[0;34m(self, how, alt, numeric_only, min_count)\u001b[0m\n\u001b[1;32m 3674\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_try_coerce_and_cast_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3675\u001b[0;31m \u001b[0mnewb\u001b[0m \u001b[0;34m=\u001b[0m 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ndim, fastpath)\u001b[0m\n\u001b[1;32m 119\u001b[0m 'implies %d' % (len(self.values),\n\u001b[0;32m--> 120\u001b[0;31m len(self.mgr_locs)))\n\u001b[0m\u001b[1;32m 121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: Wrong number of items passed 6, placement implies 10", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m~/anaconda/envs/robot-env/lib/python3.6/site-packages/pandas/core/groupby.py\u001b[0m in \u001b[0;36maggregate\u001b[0;34m(self, arg, *args, **kwargs)\u001b[0m\n\u001b[1;32m 3738\u001b[0m result = self._aggregate_multiple_funcs(\n\u001b[0;32m-> 3739\u001b[0;31m [arg], _level=_level, _axis=self.axis)\n\u001b[0m\u001b[1;32m 3740\u001b[0m result.columns = Index(\n", + "\u001b[0;32m~/anaconda/envs/robot-env/lib/python3.6/site-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m_aggregate_multiple_funcs\u001b[0;34m(self, arg, _level, _axis)\u001b[0m\n\u001b[1;32m 583\u001b[0m \u001b[0mcolg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gotitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 584\u001b[0;31m \u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maggregate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 585\u001b[0m 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occurred:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'director'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/anaconda/envs/robot-env/lib/python3.6/site-packages/pandas/core/groupby.py\u001b[0m in \u001b[0;36mf\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 1247\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1248\u001b[0m result = self.aggregate(\n\u001b[0;32m-> 1249\u001b[0;31m lambda x: npfunc(x, axis=self.axis))\n\u001b[0m\u001b[1;32m 1250\u001b[0m \u001b[0;32mif\u001b[0m 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4291\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDataFrameGroupBy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maggregate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4292\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4293\u001b[0m \u001b[0magg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maggregate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda/envs/robot-env/lib/python3.6/site-packages/pandas/core/groupby.py\u001b[0m in \u001b[0;36maggregate\u001b[0;34m(self, arg, *args, **kwargs)\u001b[0m\n\u001b[1;32m 3742\u001b[0m name=self._selected_obj.columns.name)\n\u001b[1;32m 3743\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3744\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_aggregate_generic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3745\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3746\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_index\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda/envs/robot-env/lib/python3.6/site-packages/pandas/core/groupby.py\u001b[0m in \u001b[0;36m_aggregate_generic\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m 3763\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3764\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3765\u001b[0;31m result[name] = self._try_cast(func(data, *args, **kwargs),\n\u001b[0m\u001b[1;32m 3766\u001b[0m data)\n\u001b[1;32m 3767\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda/envs/robot-env/lib/python3.6/site-packages/pandas/core/groupby.py\u001b[0m in \u001b[0;36m\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 1247\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1248\u001b[0m result = self.aggregate(\n\u001b[0;32m-> 1249\u001b[0;31m lambda x: npfunc(x, axis=self.axis))\n\u001b[0m\u001b[1;32m 1250\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_convert\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1251\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda/envs/robot-env/lib/python3.6/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mamax\u001b[0;34m(a, axis, out, keepdims)\u001b[0m\n\u001b[1;32m 2247\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2248\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2249\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mamax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2250\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2251\u001b[0m return _methods._amax(a, axis=axis,\n", + "\u001b[0;32m~/anaconda/envs/robot-env/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mstat_func\u001b[0;34m(self, axis, skipna, level, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m 7313\u001b[0m skipna=skipna)\n\u001b[1;32m 7314\u001b[0m return self._reduce(f, name, axis=axis, skipna=skipna,\n\u001b[0;32m-> 7315\u001b[0;31m numeric_only=numeric_only)\n\u001b[0m\u001b[1;32m 7316\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7317\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mset_function_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstat_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda/envs/robot-env/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_reduce\u001b[0;34m(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)\u001b[0m\n\u001b[1;32m 5770\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcoerce_to_dtypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5771\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5772\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5773\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5774\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mnunique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdropna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda/envs/robot-env/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[1;32m 262\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 263\u001b[0m data = _sanitize_array(data, index, dtype, copy,\n\u001b[0;32m--> 264\u001b[0;31m raise_cast_failure=True)\n\u001b[0m\u001b[1;32m 265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 266\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSingleBlockManager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda/envs/robot-env/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_sanitize_array\u001b[0;34m(data, index, dtype, copy, raise_cast_failure)\u001b[0m\n\u001b[1;32m 3216\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3217\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3218\u001b[0;31m \u001b[0msubarr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_convert_platform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3220\u001b[0m \u001b[0msubarr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_cast_to_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda/envs/robot-env/lib/python3.6/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mmaybe_convert_platform\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_values'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_convert_objects\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "df.groupby(['director']).max()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 True\n", + "1 True\n", + "2 True\n", + "3 True\n", + "4 True\n", + "5 True\n", + "6 True\n", + "7 True\n", + "8 True\n", + "9 True\n", + "10 True\n", + "11 True\n", + "12 True\n", + "13 True\n", + "14 True\n", + "15 True\n", + "16 True\n", + "17 True\n", + "18 True\n", + "19 True\n", + "20 True\n", + "21 True\n", + "22 True\n", + "23 True\n", + "24 True\n", + "25 True\n", + "26 True\n", + "27 True\n", + "28 True\n", + "29 True\n", + " ... \n", + "10836 False\n", + "10837 False\n", + "10838 False\n", + "10839 False\n", + "10840 False\n", + "10841 False\n", + "10842 False\n", + "10843 False\n", + "10844 False\n", + "10845 False\n", + "10846 False\n", + "10847 False\n", + "10848 False\n", + "10849 False\n", + "10850 False\n", + "10851 False\n", + "10852 False\n", + "10853 False\n", + "10854 False\n", + "10855 False\n", + "10856 False\n", + "10857 False\n", + "10858 False\n", + "10859 False\n", + "10860 False\n", + "10861 False\n", + "10862 False\n", + "10863 False\n", + "10864 False\n", + "10865 False\n", + "Length: 10866, dtype: bool" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(df['popularity']>5) | (df['release_year']>1996)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "**任务2.1: ** 简单读取\n", "\n", + "1. 读取数据表中名为 `id`、`popularity`、`budget`、`runtime`、`vote_average` 列的数据。\n", + "2. 读取数据表中前1～20行以及48、49行的数据。\n", + "3. 读取数据表中第50～60行的 `popularity` 那一列的数据。\n", "\n", - "**任务3：**请写代码，计算出数据有多少行、多少列？" + "要求：每一个语句只能用一行代码实现。" ] }, { @@ -100,6 +347,19 @@ "outputs": [], "source": [] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**任务2.2: ** 逻辑读取（Logical Indexing）\n", + "\n", + "1. 读取数据表中 **`popularity` 大于5**的所有数据。\n", + "2. 读取数据表中 **`popularity` 大于5**的所有数据且**发行年份在1996年之后**的所有数据。\n", + "2. 读取数据表中\n", + "\n", + "要求：每一个语句只能用一行代码实现。" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -246,7 +506,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.6.5" } }, "nbformat": 4,