reorganize lecture 1

Author: afeld

lecture_1.ipynb

@@ -1280,32 +1280,20 @@
     "![Diagram showing a DataFrame, Series, labels, and indexes](extras/img/data_structures-1.png)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "f-YNgxGPNRgi",
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "## How many records are in the dataset?"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
     "editable": true,
     "id": "7DPo85wSNU6q",
     "slideshow": {
-     "slide_type": "subslide"
+     "slide_type": "slide"
     },
     "tags": [],
     "toc-hr-collapsed": true,
     "toc-nb-collapsed": true
    },
    "source": [
-    "### `info()` method"
+    "## DataFrame information"
    ]
   },
   {
@@ -1386,84 +1374,71 @@
    "metadata": {
     "editable": true,
     "slideshow": {
-     "slide_type": "subslide"
+     "slide_type": "slide"
     },
     "tags": []
    },
    "source": [
-    "### `shape` attribute"
+    "## Demo"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 8,
+   "cell_type": "markdown",
    "metadata": {
     "editable": true,
     "slideshow": {
-     "slide_type": ""
+     "slide_type": "subslide"
     },
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(500000, 41)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "requests.shape"
+    "### Analysis"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "editable": true,
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": ""
     },
     "tags": []
    },
    "source": [
-    "## Demo"
+    "#### Which complaints are most common?"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 11,
    "metadata": {
     "editable": true,
-    "id": "qeYA8-rMlpJa",
     "slideshow": {
-     "slide_type": "subslide"
+     "slide_type": ""
     },
     "tags": []
    },
+   "outputs": [],
    "source": [
-    "### Exclude bad records from the DataFrame"
+    "# code goes here"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "editable": true,
-    "id": "RgP7ehPsmozX",
     "slideshow": {
-     "slide_type": ""
+     "slide_type": "subslide"
     },
     "tags": []
    },
    "source": [
-    "Let's look at the complaint types."
+    "#### What's the most frequent request per agency?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -1480,60 +1455,47 @@
    "cell_type": "markdown",
    "metadata": {
     "editable": true,
-    "id": "RrwqSmKSbiYC",
     "slideshow": {
      "slide_type": "subslide"
     },
     "tags": []
    },
    "source": [
-    "How should we go about cleaning those up?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# code goes here"
+    "- [`groupby()`](https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html#grouping) similar to [pivot tables](https://support.google.com/docs/answer/1272900) in spreadsheets\n",
+    "- [`reset_index()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.reset_index.html)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "editable": true,
+    "id": "qeYA8-rMlpJa",
     "slideshow": {
      "slide_type": "subslide"
     },
     "tags": []
    },
    "source": [
-    "### Analysis"
+    "### Exclude bad records from the DataFrame"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "editable": true,
+    "id": "RgP7ehPsmozX",
     "slideshow": {
      "slide_type": ""
     },
     "tags": []
    },
    "source": [
-    "#### Which complaints are most common?"
+    "Let's look at the complaint types."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -1550,18 +1512,19 @@
    "cell_type": "markdown",
    "metadata": {
     "editable": true,
+    "id": "RrwqSmKSbiYC",
     "slideshow": {
      "slide_type": "subslide"
     },
     "tags": []
    },
    "source": [
-    "#### What's the most frequent request per agency?"
+    "How should we go about cleaning those up?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -1574,20 +1537,6 @@
     "# code goes here"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": "subslide"
-    },
-    "tags": []
-   },
-   "source": [
-    "- [`groupby()`](https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html#grouping) similar to [pivot tables](https://support.google.com/docs/answer/1272900) in spreadsheets\n",
-    "- [`reset_index()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.reset_index.html)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {