{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyO3VCeqKEjhcmxcfSy646LE"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# Histograms\n","\n","The `.plot.hist()` method will generate a histogram. We can also use `.hist()` to generate a histogram."],"metadata":{"id":"QENK4w9tEo7o"}},{"cell_type":"code","source":["import pandas as pd, numpy as np # import statmeents\n","\n","# generate random data\n","df = pd.DataFrame({\"a\": np.random.randn(1000) + 1, \"b\": np.random.randn(1000), \"c\": np.random.randn(1000) - 1,}, columns=[\"a\", \"b\", \"c\"],)\n","df # inspect data"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"3RW43egqEtA3","executionInfo":{"status":"ok","timestamp":1706143471828,"user_tz":300,"elapsed":1227,"user":{"displayName":"Katherine Walden","userId":"17094108395123900917"}},"outputId":"6ffe5301-6419-468a-96b9-babbdc6e670f"},"execution_count":1,"outputs":[{"output_type":"execute_result","data":{"text/plain":["            a         b         c\n","0    1.810398  0.207306 -1.719783\n","1    0.229618  1.878545 -0.869958\n","2   -0.393201 -0.054595 -0.952195\n","3    1.470702  0.612677 -0.366439\n","4    2.174134  0.468061 -1.417535\n","..        ...       ...       ...\n","995  1.944827  1.185007 -2.180961\n","996  0.763507 -0.420616 -1.165496\n","997  1.142902  0.248652 -1.112617\n","998 -0.246135 -0.601180 -0.623862\n","999  1.005845 -0.106212 -0.264363\n","\n","[1000 rows x 3 columns]"],"text/html":["\n","  <div id=\"df-9a02a033-8675-4dbc-82b2-7dbba9fe012a\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>a</th>\n","      <th>b</th>\n","      <th>c</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1.810398</td>\n","      <td>0.207306</td>\n","      <td>-1.719783</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.229618</td>\n","      <td>1.878545</td>\n","      <td>-0.869958</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>-0.393201</td>\n","      <td>-0.054595</td>\n","      <td>-0.952195</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1.470702</td>\n","      <td>0.612677</td>\n","      <td>-0.366439</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2.174134</td>\n","      <td>0.468061</td>\n","      <td>-1.417535</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>995</th>\n","      <td>1.944827</td>\n","      <td>1.185007</td>\n","      <td>-2.180961</td>\n","    </tr>\n","    <tr>\n","      <th>996</th>\n","      <td>0.763507</td>\n","      <td>-0.420616</td>\n","      <td>-1.165496</td>\n","    </tr>\n","    <tr>\n","      <th>997</th>\n","      <td>1.142902</td>\n","      <td>0.248652</td>\n","      <td>-1.112617</td>\n","    </tr>\n","    <tr>\n","      <th>998</th>\n","      <td>-0.246135</td>\n","      <td>-0.601180</td>\n","      <td>-0.623862</td>\n","    </tr>\n","    <tr>\n","      <th>999</th>\n","      <td>1.005845</td>\n","      <td>-0.106212</td>\n","      <td>-0.264363</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1000 rows × 3 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9a02a033-8675-4dbc-82b2-7dbba9fe012a')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 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Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["## Other Parameters\n","\n","We can set `stacked` to `True` to create a stacked histogram."],"metadata":{"id":"2Q8LApEXE2zb"}},{"cell_type":"code","source":["df.hist(stacked=True) # stacked histogram"],"metadata":{"id":"79W8GnjYE6z0"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["We can also specify the bin size using the `bins` keyword."],"metadata":{"id":"jpnHNpB5E-OJ"}},{"cell_type":"code","source":["df.plot.hist(bins=20) # modified bin number/size"],"metadata":{"id":"6pZfhpZXFAUA"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Additional Resources\n","\n","We can use the `.hist()` method in `matplotlib` to further customize our histogram: [`matplotlib.axes.Axes.hist`](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.hist.html#matplotlib.axes.Axes.hist)\n","\n","For more on histograms:\n","- [`pandas`, \"Visualization, Histograms\"](https://pandas.pydata.org/docs/user_guide/visualization.html#visualization-hist)\n","- [`pandas.DataFrame.plot.hist`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.hist.html)\n","- [`pandas.DataFrame.hist`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.hist.html)"],"metadata":{"id":"IpXVOmS0FDSz"}}]}