singles.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jupyter Notebooks for Research\n",
    "\n",
    "## 1. Single Notebooks\n",
    "\n",
    "This will usually be your starting point when you start a notebook for a research\n",
    "project: you create a new notebook and start working.\n",
    "\n",
    "### The first few cells\n",
    "\n",
    "You'll notice that the first cell in here (above) is an import. I knew that I would use numpy\n",
    "and pandas in this so that was the very first thing I did. I think having the dependencies\n",
    "at the top is really useful as it makes it clear what you need available to run the notebook.\n",
    "You might call that cell a *setup cell*.\n",
    "\n",
    "The next thing I find myself doing is loading data. That data will be recalled throughout\n",
    "so I would consider this a setup cell too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.357440</td>\n",
       "      <td>0.377538</td>\n",
       "      <td>1.382338</td>\n",
       "      <td>1.175549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.939276</td>\n",
       "      <td>-1.143150</td>\n",
       "      <td>-0.542440</td>\n",
       "      <td>-0.548708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.208520</td>\n",
       "      <td>0.212690</td>\n",
       "      <td>1.268021</td>\n",
       "      <td>-0.807303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-3.303072</td>\n",
       "      <td>-0.806650</td>\n",
       "      <td>-0.360329</td>\n",
       "      <td>-0.880396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.152631</td>\n",
       "      <td>0.250250</td>\n",
       "      <td>0.078508</td>\n",
       "      <td>-0.903904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.357440  0.377538  1.382338  1.175549\n",
       "1 -0.939276 -1.143150 -0.542440 -0.548708\n",
       "2  0.208520  0.212690  1.268021 -0.807303\n",
       "3 -3.303072 -0.806650 -0.360329 -0.880396\n",
       "4  0.152631  0.250250  0.078508 -0.903904"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# some awesome data\n",
    "np.random.seed(seed=444)  # vcs would be a pain without this\n",
    "df = pd.DataFrame(np.random.randn(100, 4), columns=list('ABCD'))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have a DataFrame called `df` in memory. This is available globally:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0.290166</td>\n",
       "      <td>-0.231428</td>\n",
       "      <td>-0.712496</td>\n",
       "      <td>-0.755546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-1.621449</td>\n",
       "      <td>-1.223276</td>\n",
       "      <td>0.258372</td>\n",
       "      <td>0.540705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2.238800</td>\n",
       "      <td>-0.271408</td>\n",
       "      <td>0.206005</td>\n",
       "      <td>2.870687</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           A         B         C         D\n",
       "86  0.290166 -0.231428 -0.712496 -0.755546\n",
       "20 -1.621449 -1.223276  0.258372  0.540705\n",
       "59  2.238800 -0.271408  0.206005  2.870687"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here's `df` again in a different cell\n",
    "df.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Keep up the narrative\n",
    "\n",
    "A little bit of exploratory analysis is in order. I suppose first we might look at the univariate\n",
    "distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot.hist(subplots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These data seem to be as expected given how they were generated. Let's see if A and B are\n",
    "related somehow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot.scatter(x='A', y='B')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmmm... no correlation to see here.\n",
    "\n",
    "So far I think this notebook is pretty clear. Aren't notebooks awesome? Obviously.\n",
    "\n",
    "## Time for some transformations\n",
    "\n",
    "At somepoint you might want to transform your data. For whatever reason, you might\n",
    "find it convenient or even logical to modify you existing data object instead of\n",
    "creating a new one. In this example, we'll add a column to `df`.\n",
    "\n",
    "**If you're totally new to notebooks, before moving on, try restarting the kernel and\n",
    "running the next cell.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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",
       "      <th>D</th>\n",
       "      <th>A2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.357440</td>\n",
       "      <td>0.377538</td>\n",
       "      <td>1.382338</td>\n",
       "      <td>1.175549</td>\n",
       "      <td>1.092418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.939276</td>\n",
       "      <td>-1.143150</td>\n",
       "      <td>-0.542440</td>\n",
       "      <td>-0.548708</td>\n",
       "      <td>-3.021702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.208520</td>\n",
       "      <td>0.212690</td>\n",
       "      <td>1.268021</td>\n",
       "      <td>-0.807303</td>\n",
       "      <td>0.629729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-3.303072</td>\n",
       "      <td>-0.806650</td>\n",
       "      <td>-0.360329</td>\n",
       "      <td>-0.880396</td>\n",
       "      <td>-7.412793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.152631</td>\n",
       "      <td>0.250250</td>\n",
       "      <td>0.078508</td>\n",
       "      <td>-0.903904</td>\n",
       "      <td>0.555513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D        A2\n",
       "0  0.357440  0.377538  1.382338  1.175549  1.092418\n",
       "1 -0.939276 -1.143150 -0.542440 -0.548708 -3.021702\n",
       "2  0.208520  0.212690  1.268021 -0.807303  0.629729\n",
       "3 -3.303072 -0.806650 -0.360329 -0.880396 -7.412793\n",
       "4  0.152631  0.250250  0.078508 -0.903904  0.555513"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.assign(A2=2*df.A + df.B)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have a new column we can play with. Maybe we'll plot it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot.scatter(x='A', y='A2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No surprises there. Now go back and run the 5th code cell, the one with the histograms. This is what you'll see:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot.hist(subplots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are other modifications we could make. For instance, let's drop a\n",
    "column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>C</th>\n",
       "      <th>D</th>\n",
       "      <th>A2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.357440</td>\n",
       "      <td>1.382338</td>\n",
       "      <td>1.175549</td>\n",
       "      <td>1.092418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.939276</td>\n",
       "      <td>-0.542440</td>\n",
       "      <td>-0.548708</td>\n",
       "      <td>-3.021702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.208520</td>\n",
       "      <td>1.268021</td>\n",
       "      <td>-0.807303</td>\n",
       "      <td>0.629729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-3.303072</td>\n",
       "      <td>-0.360329</td>\n",
       "      <td>-0.880396</td>\n",
       "      <td>-7.412793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.152631</td>\n",
       "      <td>0.078508</td>\n",
       "      <td>-0.903904</td>\n",
       "      <td>0.555513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         C         D        A2\n",
       "0  0.357440  1.382338  1.175549  1.092418\n",
       "1 -0.939276 -0.542440 -0.548708 -3.021702\n",
       "2  0.208520  1.268021 -0.807303  0.629729\n",
       "3 -3.303072 -0.360329 -0.880396 -7.412793\n",
       "4  0.152631  0.078508 -0.903904  0.555513"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop('B', axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Easy. Now rerun that cell.\n",
    "\n",
    "Yuuk, we don't want that in our notebook. In this case, I'd suggest you just rerun\n",
    "the whole notebook, but what if your notebook takes more than a few minutes to run\n",
    "and you've just had a tea? I guess you could just clear the exception, or comment\n",
    "out df.drop, or just meticulously rerun the dependencies of your current cell...\n",
    "\n",
    "## Notebook structure\n",
    "\n",
    "If we are always going to run our notebook start to finish then maybe this isn't\n",
    "an issue but there many circumstances where this isn't practical. If you don't\n",
    "believe me, a few strategically placed calls to `time.sleep` ought to convince you. \n",
    "\n",
    "I find that when I'm working on a complicated project I often go back and work with\n",
    "earlier cells. These types of cell dependencies can wreck havoc with your workflow if\n",
    "you haven't thought about them.\n",
    "\n",
    "What strategies can we use to avoid this sort of thing?\n",
    "\n",
    "### 1. Avoid modification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>D</th>\n",
       "      <th>A2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.357440</td>\n",
       "      <td>1.175549</td>\n",
       "      <td>1.092418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.939276</td>\n",
       "      <td>-0.548708</td>\n",
       "      <td>-3.021702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.208520</td>\n",
       "      <td>-0.807303</td>\n",
       "      <td>0.629729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-3.303072</td>\n",
       "      <td>-0.880396</td>\n",
       "      <td>-7.412793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.152631</td>\n",
       "      <td>-0.903904</td>\n",
       "      <td>0.555513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         D        A2\n",
       "0  0.357440  1.175549  1.092418\n",
       "1 -0.939276 -0.548708 -3.021702\n",
       "2  0.208520 -0.807303  0.629729\n",
       "3 -3.303072 -0.880396 -7.412793\n",
       "4  0.152631 -0.903904  0.555513"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rerun to your hearts content\n",
    "df2 = df.drop('C', axis=1)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Avoiding modification can be a good way to go but its not without its drawbacks. Such as:\n",
    "- eating memory with lots semi-redundant objects\n",
    "- dirty variable names cluttering the namespace. E.g., {df2..dfN}, {df, df_norm, df_norm_drop_b,  ...}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>A</th>\n",
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       "      <td>0.357440</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.939276</td>\n",
       "      <td>-0.548708</td>\n",
       "      <td>-3.021702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.208520</td>\n",
       "      <td>-0.807303</td>\n",
       "      <td>0.629729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-3.303072</td>\n",
       "      <td>-0.880396</td>\n",
       "      <td>-7.412793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.152631</td>\n",
       "      <td>-0.903904</td>\n",
       "      <td>0.555513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         D        A2\n",
       "0  0.357440  1.175549  1.092418\n",
       "1 -0.939276 -0.548708 -3.021702\n",
       "2  0.208520 -0.807303  0.629729\n",
       "3 -3.303072 -0.880396 -7.412793\n",
       "4  0.152631 -0.903904  0.555513"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Self-contained cells\n",
    "np.random.seed(seed=444)\n",
    "df = pd.DataFrame(np.random.randn(100, 4), columns=list('ABCD'))\n",
    "df = df.assign(A2=2*df.A + df.B)\n",
    "df = df.drop('B', axis=1)\n",
    "# --\n",
    "df = df.drop('C', axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Self-containing cells is another option but as this example shows this becomes\n",
    "costly as you increasingly process the data.\n",
    "\n",
    "This could be ameliorated by persisting data in some way. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate the data and store it on disk\n",
    "np.random.seed(seed=444)\n",
    "df = pd.DataFrame(np.random.randn(100, 4), columns=list('ABCD'))\n",
    "df = df.assign(A2=2*df.A + df.B)\n",
    "df = df.drop('B', axis=1)\n",
    "df.to_pickle('singles_df.p.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>D</th>\n",
       "      <th>A2</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.357440</td>\n",
       "      <td>1.175549</td>\n",
       "      <td>1.092418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.939276</td>\n",
       "      <td>-0.548708</td>\n",
       "      <td>-3.021702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.208520</td>\n",
       "      <td>-0.807303</td>\n",
       "      <td>0.629729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-3.303072</td>\n",
       "      <td>-0.880396</td>\n",
       "      <td>-7.412793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.152631</td>\n",
       "      <td>-0.903904</td>\n",
       "      <td>0.555513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         D        A2\n",
       "0  0.357440  1.175549  1.092418\n",
       "1 -0.939276 -0.548708 -3.021702\n",
       "2  0.208520 -0.807303  0.629729\n",
       "3 -3.303072 -0.880396 -7.412793\n",
       "4  0.152631 -0.903904  0.555513"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_pickle('singles_df.p.gz')\n",
    "df = df.drop('C', axis=1)\n",
    "df.head()"
   ]
  }
 ],
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