diff --git a/100-pandas-puzzles.ipynb b/100-pandas-puzzles.ipynb
index aa4381626..9642fa1b8 100644
--- a/100-pandas-puzzles.ipynb
+++ b/100-pandas-puzzles.ipynb
@@ -46,7 +46,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "import pandas as pd"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -62,7 +64,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "pd.__version__"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -78,7 +82,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "pd.show_versions(as_json=False)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -117,7 +123,15 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "import numpy as np
+    data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],
+         'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3],
+         'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],
+         'priority': ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']}
+    labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']
+    df = pd.DataFrame(data, index=labels)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -133,7 +147,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "df.info()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -149,7 +165,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "df.head(3)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -165,7 +183,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "df.loc[:,['animal','age']]"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -181,7 +201,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "df.loc[df.index[[3, 4, 8]], ['animal', 'age']]"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -197,7 +219,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "df[df['visits'] > 3]"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -213,7 +237,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "df[df['age'].isnull()]"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -229,7 +255,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "df[(df['animal'] == 'cat') & (df['age'] < 3)]"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -245,7 +273,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "df[df['age'].between(2, 4)]"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -261,7 +291,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "df.loc['f', 'age'] = 1.5"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -277,7 +309,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "df['visits'].sum()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -293,7 +327,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "df.groupby('animal')['age'].mean()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -309,7 +345,10 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "df.loc['k'] = ['cat',4.5,2,'yes']
+    df = df.drop('k')"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -325,7 +364,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "df['animal'].value_counts()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -341,7 +382,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "df.sort_values(by=['age', 'visits'], ascending=[False, True])"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -357,7 +400,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "df['priority'] = df['priority'].map({'yes': True, 'no': False})"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -373,7 +418,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "df['animal'] = df['animal'].replace('snake', 'python')"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -389,7 +436,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "df.pivot_table(index='animal', columns='visits', values='age', aggfunc='mean')"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -444,7 +493,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "df.sub(df.mean(axis=1), axis=0)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -465,7 +516,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "df.sum().idxmin()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -481,7 +534,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "len(df.drop_duplicates(keep=False))"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -501,7 +556,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "(df.isnull().cumsum(axis=1) == 3).idxmax(axis=1)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -523,7 +580,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "df.groupby('grps')['vals'].nlargest(3).sum(level=0)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -539,7 +598,9 @@
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+   "df.groupby(pd.cut(df['A'], np.arange(0, 101, 10)))['B'].sum()"
+   ]
   },
   {
    "cell_type": "markdown",