small typo

Author: Dennis Bakhuis

B_Pandas_tips/3 - transform results back to original dataframe size.ipynb

@@ -5,14 +5,14 @@
    "metadata": {},
    "source": [
     "# Pandas tip #3: Transform .groupby() result back into original DataFrame\n",
-    "Sometime you need to get a statistic from a subgroup in a dataset and require this statistic to be in the original dataset. Previously I did this in multiple steps but this can also be achieved by a lesser known method in Pandas: `.transform()`.\n",
+    "Sometime you need to get a statistic from a subgroup in a dataset and require this statistic to be in the original dataset. Previously I did this in multiple steps but this can also be achieved with a lesser known method in Pandas: `.transform()`.\n",
     "\n",
-    "The `.transform()` method acts very similarly to the `.apply()` function and is especially powerfull after a `.groupby()`. It does a sort of `.apply()` on the groupby result and then transforms this into the length of the original DataFrame. Lets have a look a some artificial data:"
+    "The `.transform()` method acts very similarly to the `.apply()` function and is especially powerful after a `.groupby()`. It does a sort of `.apply()` on the groupby result and then transforms this into the length of the original DataFrame. Lets have a look a some artificial data:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -55,7 +55,7 @@
        "Name: group, dtype: int64"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -88,7 +88,7 @@
        "Name: spend_money, dtype: float64"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -109,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -239,7 +239,7 @@
        "[100 rows x 4 columns]"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -261,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -270,7 +270,7 @@
        "29.8"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -292,7 +292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -320,7 +320,7 @@
        "      <th>group</th>\n",
        "      <th>spend_money</th>\n",
        "      <th>group_mean</th>\n",
-       "      <th>2nd_place</th>\n",
+       "      <th>2nd_lowest</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -418,23 +418,23 @@
        "</div>"
       ],
       "text/plain": [
-       "    id group  spend_money  group_mean  2nd_place\n",
-       "0    0     A        25.01  504.031724      29.80\n",
-       "1    1     C       244.89  530.030909     111.55\n",
-       "2    2     B       736.47  506.783600      94.33\n",
-       "3    3     A       590.49  504.031724      29.80\n",
-       "4    4     A        29.80  504.031724      29.80\n",
-       "..  ..   ...          ...         ...        ...\n",
-       "95  95     C       800.59  530.030909     111.55\n",
-       "96  96     A       248.66  504.031724      29.80\n",
-       "97  97     B       536.29  506.783600      94.33\n",
-       "98  98     B       421.88  506.783600      94.33\n",
-       "99  99     C       462.63  530.030909     111.55\n",
+       "    id group  spend_money  group_mean  2nd_lowest\n",
+       "0    0     A        25.01  504.031724       29.80\n",
+       "1    1     C       244.89  530.030909      111.55\n",
+       "2    2     B       736.47  506.783600       94.33\n",
+       "3    3     A       590.49  504.031724       29.80\n",
+       "4    4     A        29.80  504.031724       29.80\n",
+       "..  ..   ...          ...         ...         ...\n",
+       "95  95     C       800.59  530.030909      111.55\n",
+       "96  96     A       248.66  504.031724       29.80\n",
+       "97  97     B       536.29  506.783600       94.33\n",
+       "98  98     B       421.88  506.783600       94.33\n",
+       "99  99     C       462.63  530.030909      111.55\n",
        "\n",
        "[100 rows x 5 columns]"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -446,7 +446,7 @@
     "    else:\n",
     "        return None\n",
     "\n",
-    "df['2nd_place'] = (df\n",
+    "df['2nd_lowest'] = (df\n",
     "    .groupby('group')['spend_money']\n",
     "    .transform(second_from_group)\n",
     ")\n",