minor edits

Author: tomvannuenen

lessons/1_Functions_and_Conditionals.ipynb

@@ -43,7 +43,9 @@
     "<a id='this'></a>\n",
     "# This Workshop\n",
     "\n",
-    "This three-part interactive workshop series is a follow-up to D-Lab's Python Fundamentals. It is intended for people who want to learn about the core structures of Python that underpin common operations for **data analysis.**"
+    "This three-part interactive workshop series is a follow-up to D-Lab's Python Fundamentals. It is intended for people who want to learn about the core structures of Python that underpin common operations for **data analysis.**\n",
+    "\n",
+    "📝 **Poll PyInt 1-1:**  Did you take D-Lab's Python Fundamentals workshop?"
    ]
   },
   {
@@ -121,7 +123,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "📝 **Poll PyInt 1-1:**  How many rows are in the data set?"
+    "📝 **Poll PyInt 1-2:**  How many rows are in the data set?"
    ]
   },
   {
@@ -344,7 +346,7 @@
    "source": [
     "In the code above, we create a function that adds 10 to whatever is passed into it as an argument.\n",
     "\n",
-    "📝 **Poll PyInt 1-2:**  What happens when we `apply()` our function to the `year` column?"
+    "📝 **Poll PyInt 1-3:**  What happens when we `apply()` our function to the `year` column?"
    ]
   },
   {
@@ -664,7 +666,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**📝 Poll PyInt 1-3:** What should our function return if `i == 6000`?"
+    "**📝 Poll PyInt 1-4:** What should our function return if `i == 6000`?"
    ]
   },
   {

solutions/2_Iteration_And_Visualization.ipynb

@@ -330,185 +330,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
-    "## 🥊 Challenge 3: Checking Method Output\n",
+    "## 🥊 Challenge 4: Check the Data Type\n",
     "\n",
-    "For each of the following methods, what type is the output?"
+    "What is the data type of the output of `describe()`?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "df = pd.read_csv('../data/gapminder_gni.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pandas.core.series.Series"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This is a Series\n",
-    "type(df['lifeExp'].value_counts(ascending=True))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pandas.core.frame.DataFrame"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This is a DataFrame\n",
-    "type(df.isnull())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pandas.core.frame.DataFrame"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This is also a dataframe\n",
-    "type(df.dropna())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pandas.core.series.Series"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This is a Series\n",
-    "type(df['country'].str[:4])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 🥊 Challenge 4: Dealing With Missing Values\n",
-    "\n",
-    "Dealing with missing values is important, even if some methods in Pandas automatically exclude them.\n",
-    "\n",
-    "1. Find the missing values of `df['gniPercap']` using the `.isna()` method. Check the [documentation](https://pandas.pydata.org/docs/reference/api/pandas.isnull.html) to see how it works.\n",
-    "2. Then, run the `sum()` of that output to see how many missing values we have in total.\n",
-    "3. Remove all missing values in the column using the `.dropna()` method. Check the [documentation](https://pandas.pydata.org/docs/reference/api/pandas.Series.dropna.html) to see how it works."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "15       730.0\n",
-       "16       320.0\n",
-       "17       860.0\n",
-       "18      1650.0\n",
-       "19      4040.0\n",
-       "         ...  \n",
-       "1315     810.0\n",
-       "1316     640.0\n",
-       "1317     620.0\n",
-       "1318     440.0\n",
-       "1319     330.0\n",
-       "Name: gniPercap, Length: 1010, dtype: float64"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# YOUR CODE HERE\n",
-    "df['gniPercap'].isna().sum()\n",
-    "df['gniPercap'].dropna() # Tip: add inplace=True as a argument to alter the DF directly\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0       1\n",
-       "1       1\n",
-       "2       1\n",
-       "3       1\n",
-       "4       1\n",
-       "       ..\n",
-       "1315    3\n",
-       "1316    3\n",
-       "1317    3\n",
-       "1318    3\n",
-       "1319    3\n",
-       "Name: continent, Length: 1320, dtype: int64"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#YOUR CODE HERE\n",
-    "df.continent.replace(df['continent'].unique(), [1,2,3,4,5])"
+    "type(df.describe())"
    ]
   },
   {