Mobile Games AB Testing with Cookie Cats/AB Testing with Cookie Cats/notebook.ipynb

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    "## 1. Of cats and cookies\n",
    "<p><a href=\"https://www.facebook.com/cookiecatsgame\">Cookie Cats</a> is a hugely popular mobile puzzle game developed by <a href=\"http://tactile.dk\">Tactile Entertainment</a>. It's a classic \"connect three\"-style puzzle game where the player must connect tiles of the same color to clear the board and win the level. It also features singing cats. We're not kidding! Check out this short demo:</p>\n",
    "<p><a href=\"https://youtu.be/GaP5f0jVTWE\"><img src=\"https://assets.datacamp.com/production/project_184/img/cookie_cats_video.jpeg\" style=\"width: 500px\"></a></p>\n",
    "<p>As players progress through the levels of the game, they will occasionally encounter gates that force them to wait a non-trivial amount of time or make an in-app purchase to progress. In addition to driving in-app purchases, these gates serve the important purpose of giving players an enforced break from playing the game, hopefully resulting in that the player's enjoyment of the game being increased and prolonged.</p>\n",
    "<p><img src=\"https://assets.datacamp.com/production/project_184/img/cc_gates.png\" alt></p>\n",
    "<p>But where should the gates be placed? Initially the first gate was placed at level 30, but in this notebook we're going to analyze an AB-test where we moved the first gate in Cookie Cats from level 30 to level 40. In particular, we will look at the impact on player retention. But before we get to that, a key step before undertaking any analysis is understanding the data. So let's load it in and take a look!</p>"
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       "      <th></th>\n",
       "      <th>userid</th>\n",
       "      <th>version</th>\n",
       "      <th>sum_gamerounds</th>\n",
       "      <th>retention_1</th>\n",
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       "   userid  version  sum_gamerounds  retention_1  retention_7\n",
       "0     116  gate_30               3        False        False\n",
       "1     337  gate_30              38         True        False\n",
       "2     377  gate_40             165         True        False\n",
       "3     483  gate_40               1        False        False\n",
       "4     488  gate_40             179         True         True"
      ]
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   "source": [
    "# Importing pandas\n",
    "# ... YOUR CODE FOR TASK 1 ...\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Reading in the data\n",
    "df = pd.read_csv('datasets/cookie_cats.csv')\n",
    "\n",
    "# Showing the first few rows\n",
    "# ... YOUR CODE FOR TASK 1 ...\n",
    "df.head()"
   ]
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    "## 2. The AB-test data\n",
    "<p>The data we have is from 90,189 players that installed the game while the AB-test was running. The variables are:</p>\n",
    "<ul>\n",
    "<li><code>userid</code> - a unique number that identifies each player.</li>\n",
    "<li><code>version</code> - whether the player was put in the control group (<code>gate_30</code> - a gate at level 30) or the group with the moved gate (<code>gate_40</code> - a gate at level 40).</li>\n",
    "<li><code>sum_gamerounds</code> - the number of game rounds played by the player during the first 14 days after install.</li>\n",
    "<li><code>retention_1</code> - did the player come back and play <strong>1 day</strong> after installing?</li>\n",
    "<li><code>retention_7</code> - did the player come back and play <strong>7 days</strong> after installing?</li>\n",
    "</ul>\n",
    "<p>When a player installed the game, he or she was randomly assigned to either <code>gate_30</code> or <code>gate_40</code>. As a sanity check, let's see if there are roughly the same number of players in each AB group. </p>"
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       "gate_40    45489\n",
       "gate_30    44700\n",
       "Name: version, dtype: int64"
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    "# Counting the number of players in each AB group.\n",
    "# ... YOUR CODE FOR TASK 2 ...\n",
    "number=df['version'].value_counts()\n",
    "number"
   ]
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      "text/plain": [
       "         userid  sum_gamerounds  retention_1  retention_7\n",
       "version                                                  \n",
       "gate_30   44700           44700        44700        44700\n",
       "gate_40   45489           45489        45489        45489"
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    "# Counting the number of players in each AB group.\n",
    "# ... YOUR CODE FOR TASK 2 ...\n",
    "number=df.groupby('version').count()\n",
    "number"
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   "source": [
    "## 3. The distribution of game rounds\n",
    "<p><img src=\"https://assets.datacamp.com/production/project_184/img/mr_waffles_smiling.png\" style=\"width:200px; float:left\"> </p>\n",
    "<p>It looks like there is roughly the same number of players in each group, nice!</p>\n",
    "<p>The focus of this analysis will be on how the gate placement affects player retention, but just for fun: Let's plot the distribution of the number of game rounds players played during their first week playing the game.</p>"
   ]
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       "      <td>9.018900e+04</td>\n",
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       "      <th>mean</th>\n",
       "      <td>4.998412e+06</td>\n",
       "      <td>51.872457</td>\n",
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       "             userid  sum_gamerounds\n",
       "count  9.018900e+04    90189.000000\n",
       "mean   4.998412e+06       51.872457\n",
       "std    2.883286e+06      195.050858\n",
       "min    1.160000e+02        0.000000\n",
       "25%    2.512230e+06        5.000000\n",
       "50%    4.995815e+06       16.000000\n",
       "75%    7.496452e+06       51.000000\n",
       "max    9.999861e+06    49854.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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    "df.describe()"
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     "data": {
      "text/plain": [
       "userid             int64\n",
       "version           object\n",
       "sum_gamerounds     int64\n",
       "retention_1         bool\n",
       "retention_7         bool\n",
       "dtype: object"
      ]
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     "execution_count": 5,
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       "0"
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     "execution_count": 6,
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   "source": [
    "df.isnull().any(axis=0).sum()"
   ]
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   "execution_count": 7,
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     "data": {
      "text/plain": [
       "userid            0\n",
       "version           0\n",
       "sum_gamerounds    0\n",
       "retention_1       0\n",
       "retention_7       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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    "df.isnull().sum(axis=0)"
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   "execution_count": 8,
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       "942"
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     "execution_count": 8,
     "metadata": {},
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   "source": [
    "df['sum_gamerounds'].nunique()"
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      "text/plain": [
       "   sum_gamerounds  userid  version  retention_1  retention_7\n",
       "0               0    3994     3994         3994         3994\n",
       "1               1    5538     5538         5538         5538\n",
       "2               2    4606     4606         4606         4606\n",
       "3               3    3958     3958         3958         3958\n",
       "4               4    3629     3629         3629         3629"
      ]
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     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "# This command makes plots appear in the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# Counting the number of players for each number of gamerounds \n",
    "gamerounds=df.groupby('sum_gamerounds').count().sort_index().reset_index()\n",
    "gamerounds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3994\n",
       "1    5538\n",
       "2    4606\n",
       "3    3958\n",
       "4    3629\n",
       "Name: userid, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=gamerounds['sum_gamerounds'][0:100]\n",
    "y=gamerounds['userid'][0:100]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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    {
     "data": {
      "text/plain": [
       "[Text(0,0.5,'players'), Text(0.5,0,'gamerounds')]"
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     },
     "execution_count": 11,
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     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cecc31eb8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure()\n",
    "ax=fig.add_subplot(111)\n",
    "ax.bar(x,y)\n",
    "ax.set(xlabel=\"gamerounds\",ylabel=\"players\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "17"
    }
   },
   "source": [
    "注意此处应采用柱状图，而不是直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "dc": {
     "key": "17"
    }
   },
   "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>index</th>\n",
       "      <th>sum_gamerounds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>4606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>3629</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  sum_gamerounds\n",
       "0      0            3994\n",
       "1      1            5538\n",
       "2      2            4606\n",
       "3      3            3958\n",
       "4      4            3629"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=df.sort_values(by='sum_gamerounds')\n",
    "sum_gamerounds_numbers=df1['sum_gamerounds'].value_counts().sort_index().reset_index().head()\n",
    "sum_gamerounds_numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "dc": {
     "key": "17"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6ceca9c7f0>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cecb9ba90>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1=sum_gamerounds_numbers['index']\n",
    "y1=sum_gamerounds_numbers['sum_gamerounds']\n",
    "plt.plot(x1,y1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "24"
    }
   },
   "source": [
    "直方图统计频数方法，注意要把大的sum_gamerounds去掉，否则图形显示不能说明问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "dc": {
     "key": "24"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ceca646d8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cecbad550>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2=df.sort_values(by='sum_gamerounds').loc[df['sum_gamerounds']<=100]\n",
    "df2['sum_gamerounds'].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "24"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 4. Overall 1-day retention\n",
    "<p>In the plot above we can see that some players install the game but then never play it (0 game rounds), some players just play a couple of game rounds in their first week, and some get really hooked!</p>\n",
    "<p>What we want is for players to like the game and to get hooked. A common metric in the video gaming industry for how fun and engaging a game is <em>1-day retention</em>: The percentage of players that comes back and plays the game <em>one day</em> after they have installed it.  The higher 1-day retention is, the easier it is to retain players and build a large player base. </p>\n",
    "<p>As a first step, let's look at what 1-day retention is overall.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "dc": {
     "key": "24"
    }
   },
   "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>userid</th>\n",
       "      <th>version</th>\n",
       "      <th>sum_gamerounds</th>\n",
       "      <th>retention_1</th>\n",
       "      <th>retention_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>116</td>\n",
       "      <td>gate_30</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>337</td>\n",
       "      <td>gate_30</td>\n",
       "      <td>38</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>377</td>\n",
       "      <td>gate_40</td>\n",
       "      <td>165</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>483</td>\n",
       "      <td>gate_40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>488</td>\n",
       "      <td>gate_40</td>\n",
       "      <td>179</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userid  version  sum_gamerounds  retention_1  retention_7\n",
       "0     116  gate_30               3        False        False\n",
       "1     337  gate_30              38         True        False\n",
       "2     377  gate_40             165         True        False\n",
       "3     483  gate_40               1        False        False\n",
       "4     488  gate_40             179         True         True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "dc": {
     "key": "24"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40153"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['retention_1'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "dc": {
     "key": "24"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4452095044850259"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['retention_1'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "dc": {
     "key": "24"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4452095044850259"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The % of users that came back the day after they installed\n",
    "# ... YOUR CODE FOR TASK 4 ...\n",
    "retention_1_rate=df['retention_1'].loc[df['retention_1']==True].count()/df['retention_1'].count()\n",
    "retention_1_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "31"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 5. 1-day retention by AB-group\n",
    "<p><img src=\"https://assets.datacamp.com/production/project_184/img/belle_cookie.png\" style=\"width:200px; float:right\"> </p>\n",
    "<p>So, a little less than half of the players come back one day after installing the game. Now that we have a benchmark, let's look at how 1-day retention differs between the two AB-groups.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "dc": {
     "key": "31"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "version\n",
       "gate_30    0.448188\n",
       "gate_40    0.442283\n",
       "Name: retention_1, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating 1-day retention for each AB-group\n",
    "# ... YOUR CODE FOR TASK 5 ...\n",
    "retention_1_rate_AB=df['retention_1'].groupby(df['version']).mean()\n",
    "retention_1_rate_AB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "dc": {
     "key": "31"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "version\n",
       "gate_30    0.448188\n",
       "gate_40    0.442283\n",
       "Name: retention_1, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating 1-day retention for each AB-group\n",
    "# ... YOUR CODE FOR TASK 5 ...\n",
    "retention_1_rate_AB=df.groupby('version').retention_1.mean()\n",
    "retention_1_rate_AB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "38"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 6. Should we be confident in the difference?\n",
    "<p>It appears that there was a slight decrease in 1-day retention when the gate was moved to level 40 (44.2%) compared to the control when it was at level 30 (44.8%). It's a small change, but even small changes in retention can have a large impact. But while we are certain of the difference in the data, how certain should we be that a gate at level 40 will be worse in the future?</p>\n",
    "<p>There are a couple of ways we can get at the certainty of these retention numbers. Here we will use bootstrapping: We will repeatedly re-sample our dataset (with replacement) and calculate 1-day retention for those samples. The variation in 1-day retention will give us an indication of how uncertain the retention numbers are.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "dc": {
     "key": "38"
    }
   },
   "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>userid</th>\n",
       "      <th>version</th>\n",
       "      <th>sum_gamerounds</th>\n",
       "      <th>retention_1</th>\n",
       "      <th>retention_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>116</td>\n",
       "      <td>gate_30</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>337</td>\n",
       "      <td>gate_30</td>\n",
       "      <td>38</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>377</td>\n",
       "      <td>gate_40</td>\n",
       "      <td>165</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>483</td>\n",
       "      <td>gate_40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>488</td>\n",
       "      <td>gate_40</td>\n",
       "      <td>179</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userid  version  sum_gamerounds  retention_1  retention_7\n",
       "0     116  gate_30               3        False        False\n",
       "1     337  gate_30              38         True        False\n",
       "2     377  gate_40             165         True        False\n",
       "3     483  gate_40               1        False        False\n",
       "4     488  gate_40             179         True         True"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "dc": {
     "key": "38"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[version\n",
       " gate_30    0.447968\n",
       " gate_40    0.442201\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.453104\n",
       " gate_40    0.440715\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449203\n",
       " gate_40    0.445150\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444657\n",
       " gate_40    0.445228\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447825\n",
       " gate_40    0.444437\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445042\n",
       " gate_40    0.444934\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449556\n",
       " gate_40    0.441234\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448959\n",
       " gate_40    0.442017\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447834\n",
       " gate_40    0.444291\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444559\n",
       " gate_40    0.443498\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449851\n",
       " gate_40    0.442879\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.452159\n",
       " gate_40    0.444729\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446233\n",
       " gate_40    0.448986\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446082\n",
       " gate_40    0.444552\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.453457\n",
       " gate_40    0.445436\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448229\n",
       " gate_40    0.440514\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448658\n",
       " gate_40    0.441328\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447304\n",
       " gate_40    0.443288\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446925\n",
       " gate_40    0.440523\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448655\n",
       " gate_40    0.445507\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450886\n",
       " gate_40    0.442695\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448703\n",
       " gate_40    0.442166\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447832\n",
       " gate_40    0.443254\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449303\n",
       " gate_40    0.442767\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.443033\n",
       " gate_40    0.443568\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451841\n",
       " gate_40    0.439600\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448380\n",
       " gate_40    0.441794\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448624\n",
       " gate_40    0.439477\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447979\n",
       " gate_40    0.441128\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447965\n",
       " gate_40    0.436809\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449428\n",
       " gate_40    0.439665\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.452615\n",
       " gate_40    0.437805\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448212\n",
       " gate_40    0.442716\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445500\n",
       " gate_40    0.440655\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.442146\n",
       " gate_40    0.444844\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451935\n",
       " gate_40    0.439377\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.44817\n",
       " gate_40    0.44059\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.44419\n",
       " gate_40    0.43882\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449288\n",
       " gate_40    0.446660\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451401\n",
       " gate_40    0.442544\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.443519\n",
       " gate_40    0.444124\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451153\n",
       " gate_40    0.443633\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447407\n",
       " gate_40    0.442836\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449857\n",
       " gate_40    0.446094\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.452002\n",
       " gate_40    0.443469\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448213\n",
       " gate_40    0.440727\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.443025\n",
       " gate_40    0.439688\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447702\n",
       " gate_40    0.442266\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449532\n",
       " gate_40    0.440956\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449604\n",
       " gate_40    0.443649\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446927\n",
       " gate_40    0.446073\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445651\n",
       " gate_40    0.444620\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.443437\n",
       " gate_40    0.442416\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451006\n",
       " gate_40    0.443006\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451196\n",
       " gate_40    0.440248\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447443\n",
       " gate_40    0.440324\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449925\n",
       " gate_40    0.440849\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450893\n",
       " gate_40    0.441956\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447343\n",
       " gate_40    0.442724\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445719\n",
       " gate_40    0.441988\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445269\n",
       " gate_40    0.444032\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449884\n",
       " gate_40    0.441229\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445483\n",
       " gate_40    0.442725\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445817\n",
       " gate_40    0.442136\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448570\n",
       " gate_40    0.445366\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447176\n",
       " gate_40    0.440725\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448384\n",
       " gate_40    0.445334\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447861\n",
       " gate_40    0.443143\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451562\n",
       " gate_40    0.441250\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448662\n",
       " gate_40    0.439741\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451172\n",
       " gate_40    0.439467\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448634\n",
       " gate_40    0.440928\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449815\n",
       " gate_40    0.440159\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446312\n",
       " gate_40    0.438761\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448953\n",
       " gate_40    0.441984\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445335\n",
       " gate_40    0.444058\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448817\n",
       " gate_40    0.441649\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.443885\n",
       " gate_40    0.441018\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451582\n",
       " gate_40    0.445934\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447316\n",
       " gate_40    0.445596\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.452613\n",
       " gate_40    0.444231\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445037\n",
       " gate_40    0.440773\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444757\n",
       " gate_40    0.444687\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446489\n",
       " gate_40    0.440879\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449167\n",
       " gate_40    0.440115\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.443729\n",
       " gate_40    0.438788\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449682\n",
       " gate_40    0.441866\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449009\n",
       " gate_40    0.444522\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444412\n",
       " gate_40    0.441254\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447699\n",
       " gate_40    0.439339\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446294\n",
       " gate_40    0.444838\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451011\n",
       " gate_40    0.444586\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.440215\n",
       " gate_40    0.441285\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444900\n",
       " gate_40    0.444766\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445163\n",
       " gate_40    0.442246\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447616\n",
       " gate_40    0.439010\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447032\n",
       " gate_40    0.441904\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451075\n",
       " gate_40    0.445404\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444517\n",
       " gate_40    0.444418\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447701\n",
       " gate_40    0.441150\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.452292\n",
       " gate_40    0.441550\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447275\n",
       " gate_40    0.447488\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447529\n",
       " gate_40    0.443331\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445337\n",
       " gate_40    0.443796\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.452856\n",
       " gate_40    0.436083\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444551\n",
       " gate_40    0.442997\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448725\n",
       " gate_40    0.438189\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445194\n",
       " gate_40    0.444568\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.452384\n",
       " gate_40    0.443414\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450659\n",
       " gate_40    0.443069\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444635\n",
       " gate_40    0.442355\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.452138\n",
       " gate_40    0.444739\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448066\n",
       " gate_40    0.439555\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451714\n",
       " gate_40    0.440559\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447986\n",
       " gate_40    0.447097\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446678\n",
       " gate_40    0.449420\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444282\n",
       " gate_40    0.439472\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446959\n",
       " gate_40    0.443932\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445393\n",
       " gate_40    0.438404\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444205\n",
       " gate_40    0.440790\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.442905\n",
       " gate_40    0.441685\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446834\n",
       " gate_40    0.440839\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450471\n",
       " gate_40    0.443475\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.452494\n",
       " gate_40    0.445678\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444109\n",
       " gate_40    0.439278\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.453365\n",
       " gate_40    0.443980\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449274\n",
       " gate_40    0.440110\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446536\n",
       " gate_40    0.441022\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448969\n",
       " gate_40    0.447627\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.443846\n",
       " gate_40    0.443934\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450068\n",
       " gate_40    0.442826\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449702\n",
       " gate_40    0.445149\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450122\n",
       " gate_40    0.443014\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448770\n",
       " gate_40    0.441973\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.440944\n",
       " gate_40    0.442574\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449256\n",
       " gate_40    0.445059\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448565\n",
       " gate_40    0.444415\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445415\n",
       " gate_40    0.445007\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444197\n",
       " gate_40    0.443146\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447569\n",
       " gate_40    0.442799\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445521\n",
       " gate_40    0.442528\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449364\n",
       " gate_40    0.437408\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449616\n",
       " gate_40    0.446055\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451193\n",
       " gate_40    0.439707\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445797\n",
       " gate_40    0.442908\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450903\n",
       " gate_40    0.444650\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445329\n",
       " gate_40    0.439544\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448859\n",
       " gate_40    0.441750\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445517\n",
       " gate_40    0.443202\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450202\n",
       " gate_40    0.442760\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449219\n",
       " gate_40    0.440482\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451107\n",
       " gate_40    0.442915\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447079\n",
       " gate_40    0.442969\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.443633\n",
       " gate_40    0.445891\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448830\n",
       " gate_40    0.441886\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447904\n",
       " gate_40    0.445735\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448506\n",
       " gate_40    0.443813\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.453797\n",
       " gate_40    0.438478\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448537\n",
       " gate_40    0.439585\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448177\n",
       " gate_40    0.442878\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451054\n",
       " gate_40    0.441865\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447578\n",
       " gate_40    0.444205\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448394\n",
       " gate_40    0.440960\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449889\n",
       " gate_40    0.442685\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446554\n",
       " gate_40    0.440943\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450335\n",
       " gate_40    0.445720\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450936\n",
       " gate_40    0.438682\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447059\n",
       " gate_40    0.444230\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448784\n",
       " gate_40    0.438575\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450892\n",
       " gate_40    0.445883\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444598\n",
       " gate_40    0.441236\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449594\n",
       " gate_40    0.444366\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446732\n",
       " gate_40    0.440096\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.453471\n",
       " gate_40    0.440888\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449477\n",
       " gate_40    0.442189\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447142\n",
       " gate_40    0.439778\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449965\n",
       " gate_40    0.439972\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449181\n",
       " gate_40    0.437042\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448327\n",
       " gate_40    0.444815\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448856\n",
       " gate_40    0.442241\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451073\n",
       " gate_40    0.442459\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.443056\n",
       " gate_40    0.444267\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446200\n",
       " gate_40    0.441641\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448195\n",
       " gate_40    0.439923\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446495\n",
       " gate_40    0.440098\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.44801\n",
       " gate_40    0.44191\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445444\n",
       " gate_40    0.439153\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.453218\n",
       " gate_40    0.442651\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448919\n",
       " gate_40    0.438527\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.453075\n",
       " gate_40    0.444880\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447433\n",
       " gate_40    0.443817\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446585\n",
       " gate_40    0.442030\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451487\n",
       " gate_40    0.441300\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446453\n",
       " gate_40    0.444185\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.452438\n",
       " gate_40    0.439329\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446927\n",
       " gate_40    0.441504\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.453555\n",
       " gate_40    0.440895\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450885\n",
       " gate_40    0.447055\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448623\n",
       " gate_40    0.445430\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450215\n",
       " gate_40    0.444836\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450238\n",
       " gate_40    0.440244\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451255\n",
       " gate_40    0.443846\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447328\n",
       " gate_40    0.444931\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444192\n",
       " gate_40    0.443104\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449792\n",
       " gate_40    0.441191\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.453581\n",
       " gate_40    0.441772\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445132\n",
       " gate_40    0.445419\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448402\n",
       " gate_40    0.444281\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446081\n",
       " gate_40    0.442476\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451344\n",
       " gate_40    0.447046\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.443024\n",
       " gate_40    0.441884\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445105\n",
       " gate_40    0.442288\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445856\n",
       " gate_40    0.437478\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450243\n",
       " gate_40    0.443270\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449149\n",
       " gate_40    0.440976\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450509\n",
       " gate_40    0.438327\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448163\n",
       " gate_40    0.437097\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448669\n",
       " gate_40    0.437325\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445975\n",
       " gate_40    0.442505\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450669\n",
       " gate_40    0.439471\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447357\n",
       " gate_40    0.442827\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449682\n",
       " gate_40    0.438799\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448752\n",
       " gate_40    0.441768\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446861\n",
       " gate_40    0.443027\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446314\n",
       " gate_40    0.441135\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447230\n",
       " gate_40    0.440701\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448449\n",
       " gate_40    0.441077\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446190\n",
       " gate_40    0.443716\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446514\n",
       " gate_40    0.443516\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.452361\n",
       " gate_40    0.447626\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447002\n",
       " gate_40    0.438593\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449204\n",
       " gate_40    0.443184\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449272\n",
       " gate_40    0.440397\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450159\n",
       " gate_40    0.443759\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450263\n",
       " gate_40    0.443103\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449337\n",
       " gate_40    0.443266\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.454617\n",
       " gate_40    0.443765\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446028\n",
       " gate_40    0.445834\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447429\n",
       " gate_40    0.441050\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449290\n",
       " gate_40    0.442474\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446788\n",
       " gate_40    0.441514\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450469\n",
       " gate_40    0.446107\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447079\n",
       " gate_40    0.442813\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447067\n",
       " gate_40    0.441998\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450356\n",
       " gate_40    0.445178\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445260\n",
       " gate_40    0.441346\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446013\n",
       " gate_40    0.440436\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449253\n",
       " gate_40    0.442955\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449606\n",
       " gate_40    0.437362\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451527\n",
       " gate_40    0.442920\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449421\n",
       " gate_40    0.440115\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448580\n",
       " gate_40    0.442803\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447725\n",
       " gate_40    0.441980\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447510\n",
       " gate_40    0.446114\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445856\n",
       " gate_40    0.443687\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448385\n",
       " gate_40    0.443513\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.443835\n",
       " gate_40    0.444231\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447587\n",
       " gate_40    0.441431\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448050\n",
       " gate_40    0.442424\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449052\n",
       " gate_40    0.440269\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447550\n",
       " gate_40    0.440212\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451149\n",
       " gate_40    0.441861\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445830\n",
       " gate_40    0.438963\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448492\n",
       " gate_40    0.440865\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448628\n",
       " gate_40    0.441881\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445537\n",
       " gate_40    0.444777\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.452758\n",
       " gate_40    0.445796\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447586\n",
       " gate_40    0.441517\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447001\n",
       " gate_40    0.442806\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446066\n",
       " gate_40    0.443002\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447901\n",
       " gate_40    0.442596\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445838\n",
       " gate_40    0.443460\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449568\n",
       " gate_40    0.437501\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.452590\n",
       " gate_40    0.445697\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446597\n",
       " gate_40    0.440592\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445102\n",
       " gate_40    0.441327\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448693\n",
       " gate_40    0.440565\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448410\n",
       " gate_40    0.438431\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448610\n",
       " gate_40    0.443161\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446741\n",
       " gate_40    0.443507\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448837\n",
       " gate_40    0.441869\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450751\n",
       " gate_40    0.441300\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.453527\n",
       " gate_40    0.444707\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446968\n",
       " gate_40    0.443900\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449658\n",
       " gate_40    0.438350\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449777\n",
       " gate_40    0.443087\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447012\n",
       " gate_40    0.440719\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447491\n",
       " gate_40    0.445722\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444959\n",
       " gate_40    0.441299\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446892\n",
       " gate_40    0.438836\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448641\n",
       " gate_40    0.440416\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450318\n",
       " gate_40    0.441033\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444937\n",
       " gate_40    0.440296\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447073\n",
       " gate_40    0.438640\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449661\n",
       " gate_40    0.439981\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445506\n",
       " gate_40    0.441995\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446600\n",
       " gate_40    0.445843\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447472\n",
       " gate_40    0.444469\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450356\n",
       " gate_40    0.441749\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449964\n",
       " gate_40    0.442637\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445082\n",
       " gate_40    0.437282\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448660\n",
       " gate_40    0.441172\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449149\n",
       " gate_40    0.444503\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447610\n",
       " gate_40    0.442622\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445390\n",
       " gate_40    0.440255\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447757\n",
       " gate_40    0.442899\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.453431\n",
       " gate_40    0.442726\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448206\n",
       " gate_40    0.439711\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448802\n",
       " gate_40    0.440389\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447698\n",
       " gate_40    0.443229\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451458\n",
       " gate_40    0.442572\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451645\n",
       " gate_40    0.444118\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448842\n",
       " gate_40    0.441826\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449028\n",
       " gate_40    0.444336\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449614\n",
       " gate_40    0.442672\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450209\n",
       " gate_40    0.440542\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448632\n",
       " gate_40    0.443088\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451655\n",
       " gate_40    0.439984\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448331\n",
       " gate_40    0.442624\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449133\n",
       " gate_40    0.441405\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448810\n",
       " gate_40    0.441659\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446313\n",
       " gate_40    0.439468\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450438\n",
       " gate_40    0.438928\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449917\n",
       " gate_40    0.441407\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444086\n",
       " gate_40    0.444769\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451151\n",
       " gate_40    0.439814\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449294\n",
       " gate_40    0.441191\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450019\n",
       " gate_40    0.439145\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447436\n",
       " gate_40    0.442179\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449389\n",
       " gate_40    0.438212\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449746\n",
       " gate_40    0.443793\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446978\n",
       " gate_40    0.444129\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449949\n",
       " gate_40    0.442339\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451124\n",
       " gate_40    0.439078\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449202\n",
       " gate_40    0.444184\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446669\n",
       " gate_40    0.439979\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449458\n",
       " gate_40    0.441562\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445788\n",
       " gate_40    0.442242\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448640\n",
       " gate_40    0.444393\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.44728\n",
       " gate_40    0.44222\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448910\n",
       " gate_40    0.441908\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448993\n",
       " gate_40    0.439725\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445917\n",
       " gate_40    0.439287\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445299\n",
       " gate_40    0.441513\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448982\n",
       " gate_40    0.441680\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449485\n",
       " gate_40    0.446025\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448057\n",
       " gate_40    0.446053\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448924\n",
       " gate_40    0.444311\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446984\n",
       " gate_40    0.439602\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447863\n",
       " gate_40    0.441808\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449764\n",
       " gate_40    0.445219\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444792\n",
       " gate_40    0.441042\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446976\n",
       " gate_40    0.444574\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449676\n",
       " gate_40    0.438926\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447180\n",
       " gate_40    0.439722\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444905\n",
       " gate_40    0.441794\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448884\n",
       " gate_40    0.439330\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450409\n",
       " gate_40    0.441089\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444810\n",
       " gate_40    0.445422\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446167\n",
       " gate_40    0.440536\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451453\n",
       " gate_40    0.443992\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449865\n",
       " gate_40    0.442841\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449647\n",
       " gate_40    0.441027\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448931\n",
       " gate_40    0.444192\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449681\n",
       " gate_40    0.441484\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448013\n",
       " gate_40    0.444219\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446871\n",
       " gate_40    0.442720\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446115\n",
       " gate_40    0.443536\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448569\n",
       " gate_40    0.440491\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450687\n",
       " gate_40    0.442805\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448270\n",
       " gate_40    0.441062\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444114\n",
       " gate_40    0.441117\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448481\n",
       " gate_40    0.446152\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445523\n",
       " gate_40    0.444966\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450003\n",
       " gate_40    0.441472\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448751\n",
       " gate_40    0.443421\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449670\n",
       " gate_40    0.443586\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449964\n",
       " gate_40    0.443721\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446211\n",
       " gate_40    0.444222\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450904\n",
       " gate_40    0.442165\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449472\n",
       " gate_40    0.442447\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444285\n",
       " gate_40    0.442061\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448583\n",
       " gate_40    0.439023\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448374\n",
       " gate_40    0.443998\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445967\n",
       " gate_40    0.442145\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446113\n",
       " gate_40    0.439830\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447459\n",
       " gate_40    0.440195\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446582\n",
       " gate_40    0.442331\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450265\n",
       " gate_40    0.442436\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447356\n",
       " gate_40    0.438170\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.443373\n",
       " gate_40    0.441034\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446275\n",
       " gate_40    0.444282\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.452053\n",
       " gate_40    0.439967\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446263\n",
       " gate_40    0.442500\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446987\n",
       " gate_40    0.436108\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.452486\n",
       " gate_40    0.438502\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449501\n",
       " gate_40    0.443511\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448135\n",
       " gate_40    0.439078\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448382\n",
       " gate_40    0.445456\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448551\n",
       " gate_40    0.443145\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446640\n",
       " gate_40    0.441799\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445916\n",
       " gate_40    0.446031\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447677\n",
       " gate_40    0.443866\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447965\n",
       " gate_40    0.440041\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451170\n",
       " gate_40    0.442323\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448419\n",
       " gate_40    0.441844\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.453531\n",
       " gate_40    0.441861\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444628\n",
       " gate_40    0.440921\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447736\n",
       " gate_40    0.437928\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450837\n",
       " gate_40    0.441639\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447539\n",
       " gate_40    0.440479\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445704\n",
       " gate_40    0.443818\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448098\n",
       " gate_40    0.439457\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.452147\n",
       " gate_40    0.437357\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447987\n",
       " gate_40    0.442333\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446244\n",
       " gate_40    0.441355\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448859\n",
       " gate_40    0.444620\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.454797\n",
       " gate_40    0.445608\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.452687\n",
       " gate_40    0.439600\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448259\n",
       " gate_40    0.445220\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450613\n",
       " gate_40    0.444542\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.452143\n",
       " gate_40    0.445697\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450267\n",
       " gate_40    0.443360\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448523\n",
       " gate_40    0.439320\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448005\n",
       " gate_40    0.441380\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446645\n",
       " gate_40    0.445950\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447144\n",
       " gate_40    0.443368\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446697\n",
       " gate_40    0.446760\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447884\n",
       " gate_40    0.438916\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446063\n",
       " gate_40    0.441315\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.442953\n",
       " gate_40    0.445500\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.443024\n",
       " gate_40    0.439316\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447906\n",
       " gate_40    0.442250\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451795\n",
       " gate_40    0.448412\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449744\n",
       " gate_40    0.436591\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445133\n",
       " gate_40    0.444846\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451630\n",
       " gate_40    0.439917\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447337\n",
       " gate_40    0.440276\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447427\n",
       " gate_40    0.445732\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447996\n",
       " gate_40    0.439507\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451710\n",
       " gate_40    0.442274\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449388\n",
       " gate_40    0.438575\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446577\n",
       " gate_40    0.442674\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447670\n",
       " gate_40    0.442216\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451009\n",
       " gate_40    0.440870\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449641\n",
       " gate_40    0.445395\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450749\n",
       " gate_40    0.442836\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447523\n",
       " gate_40    0.443095\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445550\n",
       " gate_40    0.443093\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.443497\n",
       " gate_40    0.441591\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445403\n",
       " gate_40    0.440827\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448497\n",
       " gate_40    0.441736\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445918\n",
       " gate_40    0.445177\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.443629\n",
       " gate_40    0.440813\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.443699\n",
       " gate_40    0.442604\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449074\n",
       " gate_40    0.441290\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448606\n",
       " gate_40    0.443525\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449593\n",
       " gate_40    0.440367\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447603\n",
       " gate_40    0.442619\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450939\n",
       " gate_40    0.440142\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451058\n",
       " gate_40    0.441523\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447662\n",
       " gate_40    0.444885\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449460\n",
       " gate_40    0.442221\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447336\n",
       " gate_40    0.444058\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450273\n",
       " gate_40    0.438202\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447249\n",
       " gate_40    0.442950\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448942\n",
       " gate_40    0.443659\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449692\n",
       " gate_40    0.441835\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446086\n",
       " gate_40    0.440502\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444081\n",
       " gate_40    0.441702\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450810\n",
       " gate_40    0.445224\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.443932\n",
       " gate_40    0.440905\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.443138\n",
       " gate_40    0.446307\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445809\n",
       " gate_40    0.441806\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445456\n",
       " gate_40    0.442197\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.446060\n",
       " gate_40    0.441259\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448555\n",
       " gate_40    0.440389\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451675\n",
       " gate_40    0.446215\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449813\n",
       " gate_40    0.443796\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449527\n",
       " gate_40    0.440558\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444064\n",
       " gate_40    0.441125\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450330\n",
       " gate_40    0.441371\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444130\n",
       " gate_40    0.440544\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444961\n",
       " gate_40    0.442659\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.452638\n",
       " gate_40    0.438585\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448631\n",
       " gate_40    0.440492\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448619\n",
       " gate_40    0.442672\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.445289\n",
       " gate_40    0.443882\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447958\n",
       " gate_40    0.442057\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444121\n",
       " gate_40    0.443993\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.452437\n",
       " gate_40    0.439088\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.451712\n",
       " gate_40    0.441690\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444536\n",
       " gate_40    0.441077\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.450171\n",
       " gate_40    0.443201\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447575\n",
       " gate_40    0.441510\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.447770\n",
       " gate_40    0.440944\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.444469\n",
       " gate_40    0.443098\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.448079\n",
       " gate_40    0.440827\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.452244\n",
       " gate_40    0.440148\n",
       " Name: retention_1, dtype: float64, version\n",
       " gate_30    0.449538\n",
       " gate_40    0.444052\n",
       " Name: retention_1, dtype: float64]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating an list with bootstrapped means for each AB-group\n",
    "boot_1d = []\n",
    "gate_30_1d=[]\n",
    "gate_40_1d=[]\n",
    "iterations=500\n",
    "for i in range(iterations):\n",
    "    df_bootstrap=df.sample(frac=1,replace=True)\n",
    "    boot_mean = df_bootstrap.groupby('version').retention_1.mean()\n",
    "    gate_30_1d.append(boot_mean[0])\n",
    "    gate_40_1d.append(boot_mean[1])\n",
    "    boot_1d.append(boot_mean)\n",
    "boot_1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "dc": {
     "key": "38"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 0.4495375774445542]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gate_30_1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "dc": {
     "key": "38"
    }
   },
   "outputs": [
    {
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       " 0.43850232232056785,\n",
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       " 0.4417990582229459,\n",
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       " 0.44081283703801133,\n",
       " 0.44260420103376225,\n",
       " 0.4412898168912887,\n",
       " 0.44352503103020274,\n",
       " 0.44036697247706424,\n",
       " 0.4426186291739895,\n",
       " 0.44014169728706903,\n",
       " 0.4415230585212422,\n",
       " 0.44488457560352984,\n",
       " 0.4422205020457813,\n",
       " 0.44405763891949057,\n",
       " 0.43820150824272186,\n",
       " 0.44295022267073253,\n",
       " 0.4436588793254874,\n",
       " 0.4418345252936159,\n",
       " 0.4405021954502328,\n",
       " 0.4417020154289105,\n",
       " 0.4452242231638418,\n",
       " 0.44090509666080846,\n",
       " 0.4463066690005251,\n",
       " 0.4418062700611177,\n",
       " 0.4421973577191093,\n",
       " 0.44125910966722276,\n",
       " 0.4403894015813933,\n",
       " 0.4462148774821374,\n",
       " 0.4437958126673397,\n",
       " 0.4405575838035181,\n",
       " 0.4411253983954281,\n",
       " 0.44137063122336784,\n",
       " 0.4405442713468744,\n",
       " 0.4426590358793749,\n",
       " 0.438584969681049,\n",
       " 0.44049235621358795,\n",
       " 0.44267178604528,\n",
       " 0.4438815789473684,\n",
       " 0.44205697730564947,\n",
       " 0.4439925250082445,\n",
       " 0.4390878043406412,\n",
       " 0.4416904777726095,\n",
       " 0.4410768486341442,\n",
       " 0.44320133903008413,\n",
       " 0.44150951709552344,\n",
       " 0.44094401756311746,\n",
       " 0.4430976727785614,\n",
       " 0.44082748886242334,\n",
       " 0.4401480151518469,\n",
       " 0.444052496658706]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gate_40_1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "dc": {
     "key": "38"
    }
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gate_30</th>\n",
       "      <th>gate_40</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.447968</td>\n",
       "      <td>0.442201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.453104</td>\n",
       "      <td>0.440715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.449203</td>\n",
       "      <td>0.445150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.444657</td>\n",
       "      <td>0.445228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.447825</td>\n",
       "      <td>0.444437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.445042</td>\n",
       "      <td>0.444934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.449556</td>\n",
       "      <td>0.441234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.448959</td>\n",
       "      <td>0.442017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.447834</td>\n",
       "      <td>0.444291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.444559</td>\n",
       "      <td>0.443498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.449851</td>\n",
       "      <td>0.442879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.452159</td>\n",
       "      <td>0.444729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.446233</td>\n",
       "      <td>0.448986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.446082</td>\n",
       "      <td>0.444552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.453457</td>\n",
       "      <td>0.445436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.448229</td>\n",
       "      <td>0.440514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.448658</td>\n",
       "      <td>0.441328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.447304</td>\n",
       "      <td>0.443288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.446925</td>\n",
       "      <td>0.440523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.448655</td>\n",
       "      <td>0.445507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.450886</td>\n",
       "      <td>0.442695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.448703</td>\n",
       "      <td>0.442166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.447832</td>\n",
       "      <td>0.443254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.449303</td>\n",
       "      <td>0.442767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.443033</td>\n",
       "      <td>0.443568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.451841</td>\n",
       "      <td>0.439600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.448380</td>\n",
       "      <td>0.441794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.448624</td>\n",
       "      <td>0.439477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.447979</td>\n",
       "      <td>0.441128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.447965</td>\n",
       "      <td>0.436809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>0.450810</td>\n",
       "      <td>0.445224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>0.443932</td>\n",
       "      <td>0.440905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>0.443138</td>\n",
       "      <td>0.446307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>0.445809</td>\n",
       "      <td>0.441806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>0.445456</td>\n",
       "      <td>0.442197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>0.446060</td>\n",
       "      <td>0.441259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>0.448555</td>\n",
       "      <td>0.440389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>0.451675</td>\n",
       "      <td>0.446215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>0.449813</td>\n",
       "      <td>0.443796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>0.449527</td>\n",
       "      <td>0.440558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>0.444064</td>\n",
       "      <td>0.441125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>0.450330</td>\n",
       "      <td>0.441371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>0.444130</td>\n",
       "      <td>0.440544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>0.444961</td>\n",
       "      <td>0.442659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>0.452638</td>\n",
       "      <td>0.438585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>0.448631</td>\n",
       "      <td>0.440492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>0.448619</td>\n",
       "      <td>0.442672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>0.445289</td>\n",
       "      <td>0.443882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>0.447958</td>\n",
       "      <td>0.442057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>0.444121</td>\n",
       "      <td>0.443993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>0.452437</td>\n",
       "      <td>0.439088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>0.451712</td>\n",
       "      <td>0.441690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>0.444536</td>\n",
       "      <td>0.441077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>0.450171</td>\n",
       "      <td>0.443201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>0.447575</td>\n",
       "      <td>0.441510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>0.447770</td>\n",
       "      <td>0.440944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>0.444469</td>\n",
       "      <td>0.443098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>0.448079</td>\n",
       "      <td>0.440827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>0.452244</td>\n",
       "      <td>0.440148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>0.449538</td>\n",
       "      <td>0.444052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gate_30   gate_40\n",
       "0    0.447968  0.442201\n",
       "1    0.453104  0.440715\n",
       "2    0.449203  0.445150\n",
       "3    0.444657  0.445228\n",
       "4    0.447825  0.444437\n",
       "5    0.445042  0.444934\n",
       "6    0.449556  0.441234\n",
       "7    0.448959  0.442017\n",
       "8    0.447834  0.444291\n",
       "9    0.444559  0.443498\n",
       "10   0.449851  0.442879\n",
       "11   0.452159  0.444729\n",
       "12   0.446233  0.448986\n",
       "13   0.446082  0.444552\n",
       "14   0.453457  0.445436\n",
       "15   0.448229  0.440514\n",
       "16   0.448658  0.441328\n",
       "17   0.447304  0.443288\n",
       "18   0.446925  0.440523\n",
       "19   0.448655  0.445507\n",
       "20   0.450886  0.442695\n",
       "21   0.448703  0.442166\n",
       "22   0.447832  0.443254\n",
       "23   0.449303  0.442767\n",
       "24   0.443033  0.443568\n",
       "25   0.451841  0.439600\n",
       "26   0.448380  0.441794\n",
       "27   0.448624  0.439477\n",
       "28   0.447979  0.441128\n",
       "29   0.447965  0.436809\n",
       "..        ...       ...\n",
       "470  0.450810  0.445224\n",
       "471  0.443932  0.440905\n",
       "472  0.443138  0.446307\n",
       "473  0.445809  0.441806\n",
       "474  0.445456  0.442197\n",
       "475  0.446060  0.441259\n",
       "476  0.448555  0.440389\n",
       "477  0.451675  0.446215\n",
       "478  0.449813  0.443796\n",
       "479  0.449527  0.440558\n",
       "480  0.444064  0.441125\n",
       "481  0.450330  0.441371\n",
       "482  0.444130  0.440544\n",
       "483  0.444961  0.442659\n",
       "484  0.452638  0.438585\n",
       "485  0.448631  0.440492\n",
       "486  0.448619  0.442672\n",
       "487  0.445289  0.443882\n",
       "488  0.447958  0.442057\n",
       "489  0.444121  0.443993\n",
       "490  0.452437  0.439088\n",
       "491  0.451712  0.441690\n",
       "492  0.444536  0.441077\n",
       "493  0.450171  0.443201\n",
       "494  0.447575  0.441510\n",
       "495  0.447770  0.440944\n",
       "496  0.444469  0.443098\n",
       "497  0.448079  0.440827\n",
       "498  0.452244  0.440148\n",
       "499  0.449538  0.444052\n",
       "\n",
       "[500 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transforming the list to a DataFrame\n",
    "df_boot=pd.DataFrame({'gate_30':gate_30_1d,'gate_40':gate_40_1d})\n",
    "\n",
    "df_boot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "dc": {
     "key": "38"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6cec6e3d68>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cec8dd860>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_boot.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "dc": {
     "key": "38"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6cec6f0c18>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hF7kLJUK9H0r5hiYoFfj6u6HlOKTP983xcldDb9v5fi8/1NjeS1VzFyu92LwHkJ4QQ1p8NAe1H0r5gCYoFfiaywHjuyuonCut5Sn/nU9zp/v+p8kMbzSa+TMSOFLX4fX9KnUhTVAq8DV4Kvh8dAWVlAMJ2X59P9SuqhbiosKZPyPB6/uek5HAscYOBob8u4lTBT5NUCrwNZZCeDQk++jeFBGrH+rkW+CnownsrDrDsrxkIsK9/198bmY8A0OG401dXt+3UsNpglKBr/EIpBXbN8TRSHKvgs56q+/Lz7R09VPR0Om1+58uNCfDuiorq9dKPmUvTVAq8DWW+q5AwsMzpbwflpt7xt+zK0EVpMURGS7aD6VspwlKBbbuFuio812BhEdaMcSm+GWhxK6qFmIiw1iUnWTL/iPDw5g1PV6voJTtNEGpwNZ4xFr6qkDCQ8Sq5vPDQomdVWdYmjONqAj7/nvPzYinTK+glM00QanA5qsx+EaSexW0noD2Wt8fexTtvQOU1rXbUl4+3JzMeOrbe2nt6rf1OCq0aYJSga3xCEQnQsIM3x/bcz+UH/VD7T7RgjF4/QbdC50vlNCrKGUfTVAqsDWWQvo8q8nN1zIWQdRUv+qH2lnVQlR4GFfk2NP/5DEn0xrfT/uhlJ00QanAZYyVoJxo3gMIj4CZK/zqCmrn8RYWz0y0fUr2tKnRpMRFaT+UspUmKBW42mutMfGmz3MuhtyrrCTZ3eJcDG5dfYMcqmmzvf8JrLmhijO0kk/ZSxOUClznKvgcTFCe+6FO7XAuBre9p1oZdBnb+588ijPiqWjoxOXyz9E0VODTBKUCl5MVfB5ZyyA8yi8mMNxV1UJ4mLA0d5pPjjcnI56egSFOt3b75Hgq9GiCUoGrsRTiMyHW/iatUUXGQFYJnHD+fqidx1tYkJXI1OgInxyvWCv5lM1sS1Ai8lsRaRSRQ8PW/UREykTkgIg8KSJJ7vV5ItIjIvvcjwfsiksFEScLJIbLXwd1+6Cn1bEQegeG2Hf6rG3DG42kaPpUAMo1QSmb2HkF9TCw/oJ1LwELjDGLgArgW8NeqzTGLHE/7rUxLhUMXEPQVO5s/5NHwdVgXFD1umMh7Dt9lv4hl08TVFx0BDnJsZqglG1sS1DGmG1AywXrXjTGDLqf7gCy7Tq+CnItVTDY6x8JKrvEuh/q+KuOhbDzeAsiUJLn2+bO4ox4yhs0QSl7ONkH9VfAc8Oe54vIOyLymoisHe1NInKPiOwWkd1NTU32R6n8kz8USHiER0LeGqh0LkHtOnGGuRkJJE6J9Olxi9PjqWruom9wyKfHVaHBkQQlIt8BBoFH3avqgBxjzBXAV4HHRGTEqUCNMQ8aY0qMMSVpaWm+CVj5n8ZSQCBtjtORWAqugdYqa2w+H+sfdLHnZCsrC3xfLFKcEc+Qy3CssdPnx1bBz+cJSkQ+BdwGfNQYazpSY0yfMeaM++c9QCUw29exqQDSWGrNoBsV63QkloKrreXxrT4/9MGaNnoHfNv/5DEnwxryqEKb+ZQNfJqgRGQ98A/ABmNM97D1aSIS7v65ACgC/G+qUuU/Gkr9o//JI63YKnl3IEHtOH4GgBU+ukF3uLzUOKLCw7TUXNnCzjLzPwJvAcUiUi0inwH+C4gHXrqgnHwdcEBE9gF/Bu41xjg/dozyTwO90FLpXwlKxLqKOv4auFw+PfSO42eYkxFPclyUT48L1uSFBWlxWsmnbGHbHX3GmLtHWP2bUbZ9AnjCrlhUkGmusMq6/aFAYriCa2D/H6H+AMxY4pNDDgy52H2ilQ8vn+mT441kTkb8uWnmlfImHUlCBR5PBV+6j2fRvZyCq62lD8vND1S30TMwxCoHCiQ8ijMSqG3rpa1nwLEYVHDSBKUCT2OpNf5dcoHTkbxbfLo19bwPy82d7H/yKM6wRpTQQgnlbZqgVOBpKIXUYuv+I39TeI01gWG/bwZQdbL/yUPH5FN20QSlAk/jEf/rf/IovAaG+n0yunn/oNX/tKrAuasngBmJMcTHRFChCUp5mSYoFVh626C92n8TVM5VVvOjD5r5Dtacdbz/CdyTF6bHayWf8jpNUCqweCYp9LcCCY+oWMi5Eipfsf1QO45blXNO9j95zHbPruu+914pr9AEpQKLP43BN5rCa604O+ptPcybx5od73/ymJMRT3vvIA3tfU6HooKIJigVWBpKISoeEp277+eyCq+xljY283X3D7L7RCvrZvvHeJTF6daQR2X17Q5HooKJJigVWDwFEiJORzK69IUQm2rr/VA7q1roH3KxtijVtmOMR7F7TD7th1LepAlKBQ5j/GcW3UsJC7Nu2q181bZhj7ZVNBEdEcZyH8//NJqk2CjSE6J1bijlVZqgVODobICeFv8tkBiu8FroaoTGw7bs/vWjzawsSCEmMtyW/U9EcUaCXkEpr9IEpQJHwyFrGRAJyr5+qNqzPRxr7GSdnzTveRSnT+VoYyeDQ74dLFcFL01QKnA0eCr4/GgU89EkzLAmU7Sh3PyNo80ArC3yjwIJj+KMBPoHXZw445tRNFTw0wSlAkfDYYifAbH+0e9yWYXXWsMeDfR4dbevHW0iPSGa2elTvbrfydLJC5W3aYJSgaPxMKQHwNWTR8E1MNhrJSkv6R90sa28iWuKpyN+Vsk4a/pUwkTH5FPeowlKBYahAWgqD4z+J4+81RAW6dVmvl1VLXT0DXL93HSv7dNbYiLDyUuJo1zvhVJeoglKBYYzldYgrNMDKEFFxUHOKqjc6rVdvnykgeiIMFbP8q8CCY/ijHgqGjqdDkMFCU1QKjAEUgXfcIXXQMNB6Gyc9K6MMbxU2sDaolSmRPlPeflwxRnxnDjTRU//kNOhqCCgCUoFhsZSCIuA1NlORzI+hdday+NbJ72rsvoOas72+GXznkdxejzGwNFG7YdSk6cJSgWGhsNWcopwfmDUcclYDFOSvdIP9XJpAwDXzp0+6X3ZxTPkkRZKKG/QBKUCQ0NpYNz/dKHhwx5NciqKF0sbWDwzienxMV4JzQ65KXHERIbp5IXKKzRBKf/X2wZtpwKv/8mj8FrorD8/l9UEnDrTzcGaNm5ZkOHFwLwvPEwomh6vY/Ipr9AEpfyfZwSJ9AXOxjFR54Y9mngz3+aDtQDcsjDTGxHZanZ6vDbxKa/QBKX8n2fA1UC6SXe4xGxILoQTb0x4F88cqGPJzCRmJsd6MTB7zMmIp6mjj5aufqdDUQFOE5Tyfw2HISYRErKcjmTi8tbAye3gGn/5dVVzF4dr27ltkf9fPYHODaW8x9YEJSK/FZFGETk0bF2yiLwkIkfdy2nu9SIi/ykix0TkgIgstTM2FUAaSq0bdP1saJ9xyVsLfW1Qf3Dcb33mQOA078HwBKUjSqjJsfsK6mFg/QXrvglsMcYUAVvczwFuBorcj3uAX9kcmwoEnkkKA7VAwiNvtbWcQDPf5gN1LMudxoykKV4Oyh7T46NJio2kXEeUUJNka4IyxmwDWi5YfTvwiPvnR4D3DVv/P8ayA0gSkcD4yqjs03oC+toDP0ElzJhQP9Sxxk7K6jsCpnkPQEQoTo/XKyg1aU70QaUbY+rcP9cDntvis4DTw7ardq97FxG5R0R2i8jupqYmeyNVzqvbby1nLHE2Dm+YQD/UMwfqEAmc5j2PuZkJlNV3MOSa3L1fKrQ5WiRhjDHAuH6DjTEPGmNKjDElaWn+NWGbskHdPmtE8EC8SfdCE+iH2nygluV5yaQn+O/NuSNZkJVId/8QVc3azKcmzokE1eBpunMvPaNo1gAzh22X7V6nQlndfpg+FyKinY5k8sbZD1XR0MHRxs6Aat7zWJSdCMCB6jaHI1GBzIkEtQn4pPvnTwJPDVv/CXc13yqgbVhToApFxkDtPshc7HQk3jHOfqjN+2sJE1jv56NHjKQwbSqxUeGaoNSk2F1m/kfgLaBYRKpF5DPAj4EbROQocL37OcCzwHHgGPAQ8AU7Y1MBoK0aelqCo//JY4z9UMYYNh+oY2V+il+PvTea8DBhwYxEDlSfdToUFcAi7Ny5MebuUV66boRtDfBFO+NRAcZTIJEZTAlqLex9xOqHukTiLa1r53hzF59Zm+/D4LxrYXYif9hxksEhFxHhOiaAGj/9rVH+q24fSHjgl5gPN8Z+qM0H6ggPE25eEHj9Tx6LshPpG3RxtFELJdTEaIJS/qtuP6TNgcjAuEF1TBJmQHLBJROU1bxXy+pZqSTHBdj8V8Msyk4C4KD2Q6kJGlOCEpG/iMitIqIJTflGsBVIDHeZfqgD1W2cbukJyOq94XKTY4mPiWC/9kOpCRprwvkl8BHgqIj8WESKbYxJKeioh67G4CqQ8MhdY90P1XB4xJc3H6glMly4aV7gVe8NFxYmLMxK5GCNXkGpiRlTgjLGvGyM+SiwFDgBvCwi20Xk0yISaWeAKkTV7bOWwXgFlbPKWp7acdFLLpfhmQN1rCtKIzE28P9rLcxO5EhdO32D4x/FXakxN9mJSArwKeCzwDvAz7AS1ku2RKZCW91+QCBjodOReF9SjjV1yKm3LnrpndOt1Lb1ctviwG7e81icncTAkNGpN9SEjLUP6kngdSAWeK8xZoMx5n+NMV8GptoZoApRdfshdTZExTkdifeJWFdRp96y+tqG2XygjqiIMK6fmz7KmwPL4plWocS+09oPpcZvrFdQDxlj5hljfuQZ3UFEogGMMSW2RadCV+2+4Ox/8si5Ejrq4OzJc6uMMbxwqJ51RWnExwR+8x7AjMQYMhJi2H2i1elQVAAaa4L6lxHWXdw+oZQ3dDZCR21w9j955FxpLYf1Qx2ubae2rZeb5gfH1RNYU28sy53GnpOaoNT4XTJBiUiGiCwDpojIFSKy1P24Gqu5TynvOzeCRBAnqOlzITrRKjd3e+FwPWEC1wVJ857H0txp1Jztob6t1+lQVIC53FBHN2EVRmQD9w9b3wF826aYVKjzVPBlLHI2DjuFhUPOynddQb14uIHleckBfXPuSJblTgNg76nWgJvXSjnrkldQxphHjDHXAJ8yxlwz7LHBGPMXH8WoQk3dfmvU75gEpyOxV84qaC6HrjOcaO6ivKGDm+YH9r1PI5mXmUB0RJg286lxu+QVlIh8zBjzByBPRL564evGmPtHeJtSk1O7H2YudzoK+3n6oU7v4MXGOQDcMC+4mvcAoiLCWJydpAlKjdvliiQ8Nb5TgfgRHkp5V3cLtJ0K7v4njxlLITwKTr3Fi4cbmJeZwMzk4OzaXZo7jcO1bfQO6A27auwueQVljPm1e/lPvglHhbxgHkHiQpExMGMpA1Xb2XPqSv72utlOR2SbktxpPPCa4UB1Gyvyk50ORwWIsd6oe5+IJIhIpIhsEZEmEfmY3cGpEBQKFXzD5awivH4/0aYvKJv3PJa6CyW0mU+Nx1jvg7rRGNMO3IY1Ft8s4Ot2BaVCWO0+SMqFKdOcjsQ3cq8izAxy7dTTzM0M3lbz5LgoClLj2H2ixelQVAAZa4LyNAXeCvzJGKPDEyt71O0P7hEkLjAwwyoGuT35FCLicDT2WpGfzK4TLQy5zOU3VoqxJ6jNIlIGLAO2iEgaoHfdKe/qOQutVaHTvAfsbTSUuWaylCNOh2K7lQXJdPQOcqSu3elQVIAY63Qb3wSuAkqMMQNAF3C7nYGpEFR/wFpmhs4V1NaKJvaYYlLbDoDL5XQ4tlqZnwLAzipt5lNjM54ZcucAHxaRTwAfBG60JyQVskKtQALYWt7E2eTFSF8HNFc4HY6tZiRNISc5lp3HzzgdigoQY63i+z3wU2ANsNz90FHMlXfV7oOEbIhLdToSn2ho7+VIXTvTZrtv2K1+29mAfGClux/Kpf1QagwuNxafRwkwzxijv1XKPiFWIPFaeRMAS5Ysh4OJULMbln7c4ajstbIghT/tqaa8oYO5mUE+lJWatLE28R0Cgm+QMOU/+jrgzLHQat6raCQ9IZq5MxIhaxlU73Y6JNutdN+kq818aizGmqBSgVIReUFENnkedgamQkz9QcCETIHE4JCL1482857ZaVZ5efZyaCyFvk6nQ7PVzORYspKmaKGEGpOxNvF931sHFJFi4H+HrSoA/h+QBHwOaHKv/7Yx5lmBCKrJAAAgAElEQVRvHVf5uRArkNh76iwdvYNcXTzdWpFVAsYFte9A/lpng7PZyoJktpY3YYwJ+nu/1OSMtcz8NawRJCLdP78N7J3IAY0x5caYJcaYJVj3VXUDT7pf/g/Pa5qcQkztPpiaAfHBO9zPcFvLGwkPE1bPcheEZLtrjmqCv5lvVX4KLV39HG0M7qtFNXljreL7HPBn4NfuVVnARi8c/zqg0hhz0gv7UoEsxAoktpY3sSxnGolTIq0VscmQXBAS/VCrCtz3Q2k/lLqMsfZBfRFYDbQDGGOOAtO9cPy7gD8Oe/4lETkgIr8VkREHYxORe0Rkt4jsbmpqGmkTFWj6u6yJ+0Kkea+xvZfSunbeU5z27heyl1ul5kFeLDszeQqZiTHsOK79UOrSxpqg+owx/Z4nIhIBTOp/kYhEARuAP7lX/QooBJYAdcC/j/Q+Y8yDxpgSY0xJWlraSJuoQNNw2Op/CZECia0V1herqy9MUFkl0NkAbdUOROU7IsKVhSlsr2zW+6HUJY01Qb0mIt8GpojIDVhJ5elJHvtmYK8xpgHAGNNgjBkyxriAh4AVk9y/ChQhViDxWnkT0+OjmXfhfUDZy6xlCPRDrZmVSmv3AEfqdVw+NbqxJqhvYlXXHQT+GngW+O4kj303w5r3RCRz2Gvvx7r3SoWC2n0QlwYJM5yOxHZWeXnT+fLy4dIXQFjk+YQdxDzFIW8ea3Y4EuXPxlRmboxxichGYKMxZtIdPyISB9yAlew87hORJVhNhycueE0Fs7r91tVTCJQcv3P6LO3Dy8uHi4iG6XOthB3k0hNiKEyL481jZ7hnXaHT4Sg/dckrKLF8X0SagXKg3D2b7v+bzEGNMV3GmJTh80oZYz5ujFlojFlkjNlgjKmbzDFUgBjohaYjIdO85ykvX1M0yniDmYuthB3khRJgNfPtqmqhfzC4R3FXE3e5Jr6/w6reW26MSTbGJAMrgdUi8ne2R6eCX+NhcA2GToFEeRNLc5LOl5dfaMYS6GmBttO+DcwBV81KpWdgiHdO6TTwamSXS1AfB+42xlR5VhhjjgMfAz5hZ2AqRHias0LgCqqxo5fDte0jN+95ZF5hLUOgH2pVQQphAm9W6v1QamSXS1CRxpiLejHd/VCjfAVUahzq9sOUaZCU43QktvOMXv6e2Ze4PSJ9Hkh4SPRDJU6JZGF2Etu1UEKN4nIJqn+Cryk1NiFUILG1oom0+Gjmz7jENBORU6xCiRC4ggJYXZjCvtNn6ewbdDoU5Ycul6AWi0j7CI8OYKEvAlRBbLDfGsE7BPqfBodcvF7RxDXFI5SXXyhzMdTtC4lCidWzUhl0GXZVaTOfutglE5QxJtwYkzDCI94Yo018anKajsBQf0j0P3nKy6+5VP+TR+YS6GqCjuAvZF2WO43oiDDePKYJSl1srDfqKuV9IVQg8WpZIxFhwurRysuH8/x7hEA/VExkOCV50/SGXTUiTVDKOXX7ITrRGsU7yL1a3sSy3GkkxIyh4SFjIUhYyPRDXVWYSll9B82dfU6HovyMJijlnLp9kLko6Ask6tt6OVLXzjVzxjgBQFQsJBdCQ2iM9rXGPezRdi03VxfQBKWcMTQA9YdConlva3kjwNj6nzzS51ujvIeABVmJJMREaLm5uogmKOWM5goY6guJCr5XyxuZkRjD7PSpY39T+gJorYK+4J91NjzMmn7j9aPNmBCoXFRjpwlKOSNECiT6B128cbSZq+dMv3x5+XAZC6xl4xF7AvMza4vSqDnbw/HmLqdDUX5EE5RyRt1+iJoKKbOcjsRWu0+20NU/NL7mPbCa+AAaDno/KD/kGV3DM9qGUqAJSjmlbp9VrRYW3L+CW8ubiAoP46rClPG9MXEmRCeETD/UzORYCtLi2HZUE5Q6L7j/Oij/5BqC+oOh0f9U1sjKgmTiosc09dp5IiFVKAGwriiNHcfP0Dsw5HQoyk9oglK+13wUBrqDvv/pdEs3Rxs7Lz047KV4ElSIFA68pziN3gEXb59ocToU5Sc0QSnf89yAOiO4r6C2VljNVWO+/+lC6fOhrz0k5oYCWJWfQlREmPZDqXM0QSnfq9sHEVMgpcjpSGy1tayRnORYClLjJraDdHclX4g0802JCmdlfrL2Q6lzNEEp36t9x2reCx9nv0wA6R0Y4s3K5rGNXj6a6XOtZYiMKAFWNV9FQye1Z3ucDkX5AU1QyrdcQ1YT34wrnI7EVjurWugdcHH1RJv3AKLjYVpeyFxBAaxz99dtq9CrKKUJSvlac4VVIBHkCWpreSPREWFcWTDO8vILpS+whoQKEUXTp5KZGKPNfArQBKV8rfYdaxn0CaqJqwpTiIkMn9yO0hdASyX0d3snMD8nIqwrSuP1o80MDrmcDkc5TBOU8q3ad4J+BImq5i6qmrsmXr03XPp8MC5rcscQcXVxGh29g+w52ep0KMphmqCUb9XstW7QDeIRJDyjl18920sJCqChdPL7ChBrZ6cRFR7GlrJGp0NRDgvevxLK/wwNWCNIBPn9T6+WN1GQFkdOSuzkdzYtDyJjoTF0EtTU6AhWFabwcmmD06EohzmWoETkhIgcFJF9IrLbvS5ZRF4SkaPu5TSn4lM2aDxiTbERxP1P3f2D7Dh+ZvyDw44mLNwqNw+hUnOAG+ZO53hzF5VNwT/diBqd01dQ1xhjlhhjStzPvwlsMcYUAVvcz1Ww8BRIZC11Ng4bvVV5hv5Bl/cSFMD0eSE15BHAtXPTAdhyRK+iQpnTCepCtwOPuH9+BHifg7Eob6t9B2ISYVq+05HY5tXyRmKjwlme78WL//QF0H0GOkOnTyYraQrzMhN4uTR0zlldzMkEZYAXRWSPiNzjXpdujKlz/1wPpF/4JhG5R0R2i8jupia9VyKg1L5jNe9NdGQFP2eMYWt5E6tnpRIdMcny8uHS51nLEGvmu35eOrtPttDa1e90KMohTiaoNcaYpcDNwBdFZN3wF4019/NFbRrGmAeNMSXGmJK0tAmOEq18b7DPaqYK4v6nyqZOqlt7vNu8BzDdXckXQoUSANfPnY7LWFelKjQ5lqCMMTXuZSPwJLACaBCRTAD3Un8zg0XDYXANBHWCerXMuqK/utjLX5ziUiA+M6SGPAJYMCOR9IRoXtZ+qJDlSIISkTgRiff8DNwIHAI2AZ90b/ZJ4Ckn4lM2qN1rLYM5QZU3MicjnhlJU7y/c0+hRAgJCxOunZPOa+VN9A3qJIahyKkrqHTgDRHZD+wCnjHGPA/8GLhBRI4C17ufq2BQvQfi0qypzINQR+8Ab59o4T3evnrySJ8PTWUwNGjP/v3UjfPT6eof4o2jzU6HohzgyHwHxpjjwEXTqRpjzgDX+T4iZbvqXZC9ImgLJN48doaBIeP9/ieP9Pkw1A9njsH0OfYcww+tLkwlISaCZw/Wc93ci2qmVJDztzJzFYy6W6w/rNkll982QG0tbyQ+OoJluTbdW+4Z8qgxtJr5oiLCuGFeBi+V1tM/qIPHhhpNUMp+1but5cwVzsZhE095+drZqUSG2/RfKnU2hEWEXD8UwK2LMmjvHeTNY9rMF2o0QSn7Ve8CCQ/aAomy+g7q23u52q7mPYCIaEgpCqlBYz1Wz0olPjqCZw/WXX5jFVQ0QSn7nd5lNVFFxTkdiS1ePTd6uc335aXPD8krqOiIcG6Yl86LpQ0M6BxRIUUTlLKXa8iaYiNIm/cAtpY1sSArgekJMfYeKH0etJ2C3jZ7j+OHblmYSVvPANsrzzgdivIhTVDKXk1l0N8B2cudjsQWbd0D7DnVal/13nDpC6xlY+hMXuixpiiVqdERPHtAm/lCiSYoZa/Tu6xlkCao1481MeQy9vY/eZybvDC0xuQDiIm0mvmeO1SnN+2GEE1Qyl7Vb0NsCiQXOB2JLV4tayIpNpIlM5PsP1hCFkQnhmQ/FMDtS2bQ3jt4bkgpFfw0QSl7nXwTcq4Myht0XS7DaxWNrCtKIzzMB+cn4i6UCL1KPoA1s1JJnRrFU/tqnA5F+YgmKGWfs6eh9QTkrXE6Elscqm2jubOfa+b4cFT99HnWqOYhNHmhR0R4GLctmsGWI4209Qw4HY7yAU1Qyj4n37SWuaudjcMmW8ubEIF1Rb5MUPOhrx3aTvvumH7k/Vdk0T/k4vlDWiwRCjRBKfuceANiks5XnwWZV8sbWZydRMrUaN8d1PNvGaL9UIuyE8lPjePJd7SZLxRoglL2OfEG5F4FYcH3a9bS1c++02d9U14+3PS51jIEK/kARIT3LcliZ1ULtWd7nA5H2Sz4/nIo/9BWA61VQdv/tK2iCWPwbf8TQHQ8JOWGbKEEWNV8xsCm/bVOh6JspglK2aNqm7UM0gT1ankjqVOjWDAj0fcHT18QsldQAHmpcVyRk8Rf9lZjQrBYJJRoglL2OPayNUFh+kKnI/G6IZfhtYom1s1OI8wX5eUXylwMzUehr8P3x/YTdy6bSUVDJweqQ2/Yp1CiCUp5n2sIKl+BwuuCsv/pnVOtnO0e4No5Pu5/8shaChio3efM8f3AbYsziYkM4/92h2Y1Y6gIvr8eynm1+6CnBYpucDoSW2wpayQiTFhn9+jlo5mx1FrW7nXm+H4gISaSWxZksmlfLT39OvRRsNIEpbzv2MuAQME1Tkdii1eONLIiP5mEmEhnAohLsQolakI3QQHcWTKTjr5BXjhc73QoyiaaoJT3HXvZaoaKS3E6Eq873dJNeUOHc817HllLQ/oKCmBlfjI5ybHazBfENEEp7+posAaILbrJ6Uhs8UqZNTnhdXPTnQ1kxlI4ewq6Qnca9LAw4c5l2WyvPMPplm6nw1E20ASlvKvsacDAvA1OR2KLLWWNFKTFkZ/q8OzAWe5+qBBv5rtjWTYi8Ce9igpKmqCUd5U+BamzIW2O05F4XVffIDsqz3Cd0817YJWaIyHfzDcjaQpri9L4855qhlx6T1Sw0QSlvKer2RreaN7tQTm9xhvHmukfcnHtHIeb98AaUSKtGGr2OB2J4z5cMpPatl7ePBa6zZ3ByucJSkRmisirIlIqIodF5Cvu9d8XkRoR2ed+3OLr2NQkHXkajMtKUEFoy5EG4mMiKMmb5nQolpkr4PRO676zEHb9vOkkxUbyv29rM1+wceIKahD4e2PMPGAV8EURmed+7T+MMUvcj2cdiE1NxoH/g5RZQTl6uctleKWsiauLpxMZ7icND7lroLfNmh8qhEVHhPPBpdm8cLiepo4+p8NRXuTz/2nGmDpjzF73zx3AESDL13EoL2s+Bqe2wxUfC8rmvYM1bTR39vlH/5NH7lXW8sSbzsbhB+5emcOgy2jJeZBx9KugiOQBVwA73au+JCIHROS3IuIn7ShqTPY9ChIOi+92OhJbbDnSQJjAe5waPWIkSTMhKQdOvuF0JI4rTJvKlQUp/HHXKS2WCCKOJSgRmQo8AfytMaYd+BVQCCwB6oB/H+V994jIbhHZ3dTU5LN41SUMDcK+x6yhjeIznI7GFi8cbqAkL5lpcVFOh/JuuWvg5PaQnAL+Qh9dlUN1aw/bjurfhWDhSIISkUis5PSoMeYvAMaYBmPMkDHGBTwErBjpvcaYB40xJcaYkrQ0P/o2G8oqnoPOeqt5Lwgdb+qkvKGDmxf4YfLNvQq6z0BTudOROO7GeRmkTo3i0R2nnA5FeYkTVXwC/AY4Yoy5f9j6zGGbvR8I3QlvAs1bv4TEHJh9s9OR2OKFww0A3DTfDxNU3mpr6Zl/K4RFRYTxoZKZvFLWoLPtBgknrqBWAx8Hrr2gpPw+ETkoIgeAa4C/cyA2NV41e63iiFX3QniE09HY4vlDdSzOTmRG0hSnQ7nYtHzrcewlpyPxC3evyMEAj2vJeVDw+V8UY8wbwEhlXlpWHoje+gVExcMVH3c6ElvUnO1hf3Ub/7C+2OlQRiYCs2+CPQ9DfzdExTodkaNmJsfyntlpPL7rFF++dpb/3BKgJkQ/PTVxzUfh8F9g2SchJsHpaGzxonsqh/X+2LznUXQjDPbCidedjsQvfHRlLo0dfWw50uh0KGqSNEGpiXvtPoiIgdVfcToS2zx/qJ7Z6VMpSJvqdCijy1sDkbFQ8YLTkfiFa4rTyEyM4Q87TjodipokTVBqYpoq4NCfYflnYaof3bzqRQ3tvew60cL6BZmX39hJEdFQeC2UPRPywx4BRISH8dGVObxxrJmKhg6nw1GToAlKTcxr/wYRU4L66unp/bUYA7cvmeF0KJe34A6r1P+kjioB8JGVuURHhPG7N6ucDkVNgiYoNX51++HQE7DyHohLdToa22zcV8PCrEQK/bl5z2P2eoiaCgf/5HQkfiE5LooPLM3iL3traOnqdzocNUGaoNT4GAMvfAdik2FN8N4JcKyxk0M17bzvigAZJjIqFubcas3HNagDpgL81ep8+gZdPLZT+6IClSYoNT5lz1jVYtd8G2ISnY7GNk/tqyFM4L2L/bz/abhFH7ZGNy99yulI/EJRejxri1L5n7dO0j/ocjocNQGaoNTYDfbDS9+zZstd+imno7GNMYaN+2pYPSuV6fExToczdgXXWNOd7HrQ6Uj8xmfW5NPY0cfmA7VOh6ImQBOUGrudD0DLcbjxh0E7agTAW5VnON3SwweWBkjznkdYGCz/HFS/bY3woVhXlEZxejy/3FqJS0c5DziaoNTYnD0NW39sdcYXXe90NLb6w86TJMVGcrO/l5ePZMlHrJE93rj/8tuGgLAw4cvXzeJYYyfPHap3Ohw1Tpqg1Ng89w3AwC0/cToSWzW29/Li4QbuXJZNTGS40+GMX0wCXPlFOPI01O5zOhq/cPOCTArT4vj5K0f1KirAaIJSl1f2DJQ/A+/5hjVBXhD7v92nGXQZPrIy1+lQJu7KL0BMErzyA50nCggPE758bRFl9R28WKpXUYFEE5S6tN42ePYfYPo865t5EOsfdPGHHadYMyuV/NQ4p8OZuJhEWPd1OPYylG12Ohq/cNuiTArS4vjJC+UMDGlFX6DQBKUu7flvQUctbPg5hEc6HY2tNr5TQ317L59bV+B0KJO38q8hfYH15aK33eloHBcRHsY318+hsqmLx3fphIaBQhOUGl3ZM7DvUVjzVcgucToaWw25DA+8Vsn8GQmsKwqC0THCI+G9P4POBnj6b7SpD7hhXjor85P5j5eP0t474HQ4agw0QamRdTTA01+BjIVW31OQ23ygluPNXXz+6kKsSZ+DQHYJXPtdOPwkvP3fTkfjOBHhe7fNo7W7n/ueL3M6HDUGmqDUxYYG4c+fhr5OeP+DEBHldES26h0Y4r7ny5mXmcAtgVhafimr/9aaL+r5b0LFi05H47gFWYn81ep8/rDjFG9VnnE6HHUZmqDUxbb8kzUq9ob/hPR5Tkdju9++WUXN2R6+e+tcwsKC5OrJIywM7vgNpM+H//sEnNDRzr92YzG5KbF844kDdPYNOh2OugRNUOrdDvwfbP9Pa56nRR9yOhrbnWju4udbjnH93HSumhUEfU8jiUmAjz4BSTPhDx+w+hZD2JSocO67YxHVrd1884kDGO2f81uaoNR5la/Cxi9A3lq46V+djsZ2A0Muvvan/USECz9433ynw7HX1DT49PPWldTjH4VX/zWkJzdcWZDC124qZvOBOh56/bjT4ahRaIJSltO74H8/Dqmz4cN/sGZpDXI/fOYIu0+28oPbF5CZOMXpcOwXlwKffBoW32VNOPnbm0J6zL7Pv6eQWxdm8q/PlvHEnmqnw1Ej0ASlrH6J37/f+pb9sT/DlCSnI7LdL149xsPbT/Dp1XmBM+eTN0TFwfsfgA/8N7SehIeuhT9+BKq2gSu0bmAVEe7/8GJWz0rhH544oPdH+SEJ5PbXkpISs3v3bqfDCGyHn4QnP2/1T3xiEyQEWRXbBfoHXfzrs0d4ePsJ3rdkBj+9czER4SH6Pa23Hbb/HHb/BrrPwNQMKF4P+esgewUkZkOwlNxfQlffIJ9/dC/bKpr4m2tn8ZXrZxMebMUyfkZE9hhjLntzpSaoUDU0CFt/BK//1PpjdNdj1hVUkBpyGV4ta+S+F8qoaOjkr1bn851b5+ofIoCBHjiy2RoW6dgW6O+w1sdnwswVkLvGSlppxUGbsAaGXHzrLwf5855qVuYnc/+Hl5CVFALNvg7RBKVG13gENn4eat+BKz4Gt94f8H1OQy5De88AZ3sGONvdz9meAdq6B2jt7qe8voNtFU3UtvUyM3kK/3jbfK6fl+50yP5paAAaDsHpt+H0Tji1A9rd/TNx06HwGmtq+VnXW82FQcQYw5/3VPOPmw7jMoZ731PIX68rZEpUAI5q7+cCNkGJyHrgZ0A48N/GmB+Ptq0mqHFqq4Ft98He31v9TLfeD/Pf53RUY2KMobGjj7L6Dsrq2jna2El9Wy+NHb00dvRxtnv0oWviYyJYmZ/CB5Zmcf3cdKIiQrRJbyKMgdYTcOJ1q5/q2MvQ0woRMVB4Hcy9DYpusgowgsTplm5+/FwZzxysIyUuio+uyuVjK3OYnhBAsyv7uYBMUCISDlQANwDVwNvA3caY0pG21wQ1Bj1n4fhW6/6miudBwqDk09bwRXH+ed9Pd/8g5fUdlNd3WAmpvp3y+g5ahyWh6fHRzEiawvT4aKYnRJMSF01SbKT1mBJFYmwkSVMiSYqNImlKZPDdgOuUoUE4td2ab6rsGWivAQSyllpXVYXXQsYiiIp1OtJJ23OyhV9tPc6WsgYAlucls35+BqsKUijOiNfm4UkI1AR1JfB9Y8xN7uffAjDG/Gik7SedoDrqYbDXPZCm+9/hwn+PEV8ztrzW0N4HxrhXGQzuqir3JGvG/R5jzMX7HBogrO8s4T0thPc0E9V6jKjWCqJaKhAzxGBMCm1zPsTZeR9nIGHmu07T8/P5/Y/wTzDK6+eiMOaC5+/e4vw+rEKFjt4BOnoH6egdpLW7n5qzPdS09ljLsz3nto+NCmd2ejxzM+MpTo+nOCOBORnxTIsL7uGXAoIxVjPx0Zfg2EtQvRsw1peg1GKrzyoxGxKyIC7NahKMnmotw6Os7UZ8+Ncf/tOtPbx4uJ7XKpo43twFQGxkOPlpcWQlxTI1o5DUqVEkur8QTYkMJyJciAwLIzJCiAgLGzGZXbhmpNOWi7byj3+e9ISYSbVEBGqC+iCw3hjzWffzjwMrjTFfGmn7SSeo391iDekThKpNKhWubA6ZPLYNLWKfmcUgEU6HNaLwMCEjIYYZSTHMSJpCYdpUijPimZMRz8xpsXr1Eyi6W+DUW1C335rNt6XSalYe7HE6Mtt0E8P8vt+G3GDxz31lLXMzEyb8/rEmKP/8i3UJInIPcA9ATs4kZ3dd81WrSMDa87CvJuI52AivcYnXLvW+EV4799z6eUdVC4Mug+d7k0EQz5eUc+8Lcz89H48RwRDOYHQSfVHTGIxKwhUehQgUAIXu/XveIhfuctjai7eRUd9zftt3v+ly740KDyM+JpL4mAj3I1KbS4JBbLJVQDHn1vPrjLESV08L9HdaAxD3d4JrEIzr/MPl+TmwRreIDYugYv7NtPUMcLbbKtDpHXAx4HIxOGQYGHIxMOS6uFWCd68YKcGNuM6LsU9GZqJv+uP8LUHVADOHPc92rzvHGPMg8CBYV1CTOlrR9ZN6u7etKnY6AqW8TMQqoAiiIooLRQKpU6NJnRrYlbD+yN/Kmd4GikQkX0SigLuATQ7HpJRSygF+dQVljBkUkS8BL2CVmf/WGHPY4bCUUko5wK8SFIAx5lngWafjUEop5Sx/a+JTSimlAE1QSiml/JQmKKWUUn5JE5RSSim/pAlKKaWUX9IEpZRSyi/51Vh84yUiTcBJm3afCjTbtG9f03PxT3ou/knPxX65xpjLzpAa0AnKTiKyeyyDGQYCPRf/pOfin/Rc/Ic28SmllPJLmqCUUkr5JU1Qo3vQ6QC8SM/FP+m5+Cc9Fz+hfVBKKaX8kl5BKaWU8kuaoJRSSvmlkEhQIrJeRMpF5JiIfPMS290hIkZEStzPV4jIPvdjv4i8f7z79DabzuWEiBx0v7bbF+cx7NgTOp9h63NEpFNEvjbefXqTTecRcJ+LiOSJSM+w37UHhm27zH0+x0TkP0VEAvQ8trr36Xltut3nMezYE/49E5FFIvKWiBx2fw4x7vU+/1zGzBgT1A+siQ8rgQIgCtgPzBthu3hgG7ADKHGviwUi3D9nAo1Yc2iNaZ+BcC7u5yeA1ED6bIa99mfgT8DXxrNPfz+PQP1cgDzg0Cj73QWsAgR4Drg5QM9j64WfXwB8LhHAAWCx+3kKEO7E5zKeRyhcQa0Ajhljjhtj+oHHgdtH2O4HwL8BvZ4VxphuY8yg+2kM4KkoGes+vc2Oc3HShM8HQETeB1QBw2ddduKzseM8nDSp8xmJiGQCCcaYHcb6q/g/wPu8GPNIvH4eDpvM+dwIHDDG7Acwxpwxxgw59LmMWSgkqCzg9LDn1e5154jIUmCmMeaZC98sIitF5DBwELjX/Uf+svu0iR3nAlayelFE9ojIPfaEPqIJn4+ITAW+AfzTePdpAzvOAwLwc3HLF5F3ROQ1EVk7bJ/Vl9qnDew4D4/fuZv3vufDJrHJnM9swIjICyKyV0T+Ydg+ff25jJnfTfnuayISBtwPfGqk140xO4H5IjIXeEREnvNheOMykXMxxvQCa4wxNe629JdEpMwYs81ngY/iMufzfeA/jDGd/tRkPpJJnEcgfi51QI4x5oyILAM2ish8X8Y3VhM5D2NMO/BR9+cSDzwBfBzrysNRlzmfCGANsBzoBraIyB6gzWcBTkAoJKgaYOaw59nudR7xwAJgq/sPRAawSUQ2GGPOdUwbY46ISKd728vt0y52nMtuY0yNe32jiDyJ1ZTgiz+EEz4fYCXwQRG5D0gCXCLSC+y5zD7t4PXzMMb8VyB+Lu7fsz533HtEpBLr23uNez+j7dMOdlWZq3UAAAFxSURBVJzH8P8vHSLyGNbn4osENZnfs2pgmzGmGUBEngWWAn/A95/L2DndCWb3AysJHwfyOd+xOP8S22/lfMdiPucLCXKBWqzRgce1Tz8/lzgg3r0+DtgOrPf3z+aC9d/nfJGEzz8bm84jID8XII3zne8FWH/skt3PL+yMvyXQzsO9z1T3+kis4pZ7A+BzmQbsxV0sBbwM3OrE5zKeR9BfQRljBkXkS8ALWFUwvzXGHBaRf8b6NrTpEm9fA3xTRAYAF/AFc/4byEX7tPVEsOdcRKQAeNL9jSsCeMwY87y9Z2KZ5PmMa59eDXyMx5zMeQDpBObnsg7452G/Z/caY1rcr30BeBiYgvWH0NbmcjvOQ0TigBdEJNK9z5eBh+w8D4/JnI8xplVE7gfexurbfNac76fy6ecyHjrUkVJKKb8UClV8SimlApAmKKWUUn5JE5RSSim/pAlKKaWUX9IEpZRSyi9pglJKKeWXNEEppZTyS/8f6GLuuxSNB0gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cec63f470>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A Kernel Density Estimate plot of the bootstrap distributions\n",
    "# ... YOUR CODE FOR TASK 6 ...\n",
    "df_boot.plot.kde()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "46"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 7. Zooming in on the difference\n",
    "<p>These two distributions above represent the bootstrap uncertainty over what the underlying 1-day retention could be for the two AB-groups. Just eyeballing this plot, we can see that there seems to be some evidence of a difference, albeit small. Let's zoom in on the difference in 1-day retention</p>\n",
    "<p>(<em>Note that in this notebook we have limited the number of bootstrap replication to 500 to keep the calculations quick. In \"production\" we would likely increase this to a much larger number, say, 10 000.</em>)</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "dc": {
     "key": "46"
    },
    "tags": [
     "sample_code"
    ]
   },
   "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>gate_30</th>\n",
       "      <th>gate_40</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.447968</td>\n",
       "      <td>0.442201</td>\n",
       "      <td>1.304086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.453104</td>\n",
       "      <td>0.440715</td>\n",
       "      <td>2.811150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.449203</td>\n",
       "      <td>0.445150</td>\n",
       "      <td>0.910524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.444657</td>\n",
       "      <td>0.445228</td>\n",
       "      <td>-0.128125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.447825</td>\n",
       "      <td>0.444437</td>\n",
       "      <td>0.762346</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    gate_30   gate_40      diff\n",
       "0  0.447968  0.442201  1.304086\n",
       "1  0.453104  0.440715  2.811150\n",
       "2  0.449203  0.445150  0.910524\n",
       "3  0.444657  0.445228 -0.128125\n",
       "4  0.447825  0.444437  0.762346"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding a column with the % difference between the two AB-groups\n",
    "df_boot['diff'] = 100*(df_boot['gate_30']-df_boot['gate_40'])/df_boot['gate_40']\n",
    "df_boot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "dc": {
     "key": "46"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,0,'diff')]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cec86ae80>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ploting the bootstrap % difference\n",
    "ax = df_boot['diff'].plot.kde()\n",
    "ax.set(xlabel='diff')\n",
    "# ... YOUR CODE FOR TASK 7 ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "53"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 8. The probability of a difference\n",
    "<p><img src=\"https://assets.datacamp.com/production/project_184/img/ziggy_smiling.png\" style=\"width:200px; float:left\"> </p>\n",
    "<p>From this chart, we can see that the most likely % difference is around 1% - 2%, and that most of the distribution is above 0%, in favor of a gate at level 30. But what is the <em>probability</em> that the difference is above 0%? Let's calculate that as well.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "dc": {
     "key": "53"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_boot['diff'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "dc": {
     "key": "53"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "480"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_boot['diff'][df_boot['diff']>0].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "dc": {
     "key": "53"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96.00%\n"
     ]
    }
   ],
   "source": [
    "# Calculating the probability that 1-day retention is greater when the gate is at level 30\n",
    "prob = df_boot['diff'].loc[df_boot['diff']>0].count()/df_boot['diff'].count()\n",
    "\n",
    "# Pretty printing the probability\n",
    "# ... YOUR CODE FOR TASK 8 ...\n",
    "print('%.2f%%' % (prob*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "dc": {
     "key": "53"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96.00%\n"
     ]
    }
   ],
   "source": [
    "# Calculating the probability that 1-day retention is greater when the gate is at level 30\n",
    "prob = (df_boot['diff']>0).mean()\n",
    "\n",
    "# Pretty printing the probability\n",
    "# ... YOUR CODE FOR TASK 8 ...\n",
    "print('%.2f%%' % (prob*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "dc": {
     "key": "53"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96.00%\n"
     ]
    }
   ],
   "source": [
    "# Calculating the probability that 1-day retention is greater when the gate is at level 30\n",
    "prob = (df_boot['diff']>0).sum()/len(df_boot['diff'])\n",
    "\n",
    "# Pretty printing the probability\n",
    "# ... YOUR CODE FOR TASK 8 ...\n",
    "print('%.2f%%' % (prob*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "60"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 9. 7-day retention by AB-group\n",
    "<p>The bootstrap analysis tells us that there is a high probability that 1-day retention is better when the gate is at level 30. However, since players have only been playing the game for one day, it is likely that most players haven't reached level 30 yet. That is, many players won't have been affected by the gate, even if it's as early as level 30. </p>\n",
    "<p>But after having played for a week, more players should have reached level 40, and therefore it makes sense to also look at 7-day retention. That is: What percentage of the people that installed the game also showed up a week later to play the game again.</p>\n",
    "<p>Let's start by calculating 7-day retention for the two AB-groups.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "dc": {
     "key": "60"
    }
   },
   "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",
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       "    }\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>userid</th>\n",
       "      <th>version</th>\n",
       "      <th>sum_gamerounds</th>\n",
       "      <th>retention_1</th>\n",
       "      <th>retention_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>116</td>\n",
       "      <td>gate_30</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>337</td>\n",
       "      <td>gate_30</td>\n",
       "      <td>38</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>377</td>\n",
       "      <td>gate_40</td>\n",
       "      <td>165</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>483</td>\n",
       "      <td>gate_40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>488</td>\n",
       "      <td>gate_40</td>\n",
       "      <td>179</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userid  version  sum_gamerounds  retention_1  retention_7\n",
       "0     116  gate_30               3        False        False\n",
       "1     337  gate_30              38         True        False\n",
       "2     377  gate_40             165         True        False\n",
       "3     483  gate_40               1        False        False\n",
       "4     488  gate_40             179         True         True"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "dc": {
     "key": "60"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "version\n",
       "gate_30    0.190201\n",
       "gate_40    0.182000\n",
       "Name: retention_7, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating 7-day retention for both AB-groups\n",
    "# ... YOUR CODE FOR TASK 9 ...\n",
    "df.groupby('version').retention_7.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "67"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 10. Bootstrapping the difference again\n",
    "<p>Like with 1-day retention, we see that 7-day retention is slightly lower (18.2%) when the gate is at level 40 than when the gate is at level 30 (19.0%). This difference is also larger than for 1-day retention, presumably because more players have had time to hit the first gate. We also see that the <em>overall</em> 7-day retention is lower than the <em>overall</em> 1-day retention; fewer people play a game a week after installing than a day after installing.</p>\n",
    "<p>But as before, let's use bootstrap analysis to figure out how certain we should be of the difference between the AB-groups.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "dc": {
     "key": "67"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [],
   "source": [
    "# Creating a list with bootstrapped means for each AB-group\n",
    "boot_7d = []\n",
    "gate_30_7=[]\n",
    "gate_40_7=[]\n",
    "for i in range(500):\n",
    "    boot_mean = df.sample(frac=1,replace=True).groupby('version').retention_7.mean()\n",
    "    gate_30_7.append(boot_mean[0])\n",
    "    gate_40_7.append(boot_mean[1])\n",
    "    boot_7d.append(boot_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "dc": {
     "key": "67"
    }
   },
   "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>gate_30_7</th>\n",
       "      <th>gate_40_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.194153</td>\n",
       "      <td>0.183708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.187462</td>\n",
       "      <td>0.182626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.190259</td>\n",
       "      <td>0.182945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.186404</td>\n",
       "      <td>0.179425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.190064</td>\n",
       "      <td>0.180361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gate_30_7  gate_40_7\n",
       "0   0.194153   0.183708\n",
       "1   0.187462   0.182626\n",
       "2   0.190259   0.182945\n",
       "3   0.186404   0.179425\n",
       "4   0.190064   0.180361"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transforming the list to a DataFrame\n",
    "# ... YOUR CODE FOR TASK 10 ...\n",
    "df_boot_7=pd.DataFrame({'gate_30_7':gate_30_7,'gate_40_7':gate_40_7})\n",
    "df_boot_7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "dc": {
     "key": "67"
    }
   },
   "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>gate_30_7</th>\n",
       "      <th>gate_40_7</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.194153</td>\n",
       "      <td>0.183708</td>\n",
       "      <td>5.685810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.187462</td>\n",
       "      <td>0.182626</td>\n",
       "      <td>2.648441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.190259</td>\n",
       "      <td>0.182945</td>\n",
       "      <td>3.998115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.186404</td>\n",
       "      <td>0.179425</td>\n",
       "      <td>3.889589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.190064</td>\n",
       "      <td>0.180361</td>\n",
       "      <td>5.379781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gate_30_7  gate_40_7      diff\n",
       "0   0.194153   0.183708  5.685810\n",
       "1   0.187462   0.182626  2.648441\n",
       "2   0.190259   0.182945  3.998115\n",
       "3   0.186404   0.179425  3.889589\n",
       "4   0.190064   0.180361  5.379781"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding a column with the % difference between the two AB-groups\n",
    "df_boot_7['diff'] = (df_boot_7['gate_30_7']-df_boot_7['gate_40_7'])/df_boot_7['gate_40_7']*100\n",
    "df_boot_7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "dc": {
     "key": "67"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'% difference in means')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cec6fd240>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ploting the bootstrap % difference\n",
    "ax = df_boot_7['diff'].plot.kde()\n",
    "ax.set_xlabel(\"% difference in means\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "dc": {
     "key": "67"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.80%\n"
     ]
    }
   ],
   "source": [
    "# Calculating the probability that 7-day retention is greater when the gate is at level 30\n",
    "prob = (df_boot_7['diff']>0).mean()\n",
    "# Pretty printing the probability\n",
    "# ... YOUR CODE FOR TASK 10 ...\n",
    "print('%.2f%%' % (prob*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "74"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 11.  The conclusion\n",
    "<p>The bootstrap result tells us that there is strong evidence that 7-day retention is higher when the gate is at level 30 than when it is at level 40. The conclusion is: If we want to keep retention high — both 1-day and 7-day retention — we should <strong>not</strong> move the gate from level 30 to level 40. There are, of course, other metrics we could look at, like the number of game rounds played or how much in-game purchases are made by the two AB-groups. But retention <em>is</em> one of the most important metrics. If we don't retain our player base, it doesn't matter how much money they spend in-game.</p>\n",
    "<p><img src=\"https://assets.datacamp.com/production/project_184/img/cookie_yellow.png\" style=\"width:100px; float:center\"> </p>\n",
    "<p>So, why is retention higher when the gate is positioned earlier? One could expect the opposite: The later the obstacle, the longer people are going to engage with the game. But this is not what the data tells us. The theory of <em>hedonic adaptation</em> can give one explanation for this. In short, hedonic adaptation is the tendency for people to get less and less enjoyment out of a fun activity over time if that activity is undertaken continuously. By forcing players to take a break when they reach a gate, their enjoyment of the game is prolonged. But when the gate is moved to level 40, fewer players make it far enough, and they are more likely to quit the game because they simply got bored of it. </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "dc": {
     "key": "74"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# So, given the data and the bootstrap analysis\n",
    "# Should we move the gate from level 30 to level 40 ?\n",
    "move_to_level_40 = False\n",
    "move_to_level_40"
   ]
  }
 ],
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