2019.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas recently contucted a user survey to help guide future development.\n",
    "Thanks to everyone who participated! This post presents the high-level results.\n",
    "\n",
    "This analysis and the raw data can be found [on GitHub](https://github.com/pandas-dev/pandas-user-surveys) and run on Binder\n",
    "\n",
    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/pandas-dev/pandas-user-surveys/master?filepath=2019.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plt.rcParams['figure.dpi'] = 150\n",
    "\n",
    "df = pd.read_csv(\"data/2019.csv.gz\", parse_dates=['Timestamp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We had about 1250 repsonses over the 15 days we ran the survey in the summer of 2019."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# About the Respondents\n",
    "\n",
    "There was a fair amount of representation across pandas experience and frequeny of use, though the majority of respondents are on the more experienced side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "order = [\n",
    "    'Less than 3 months',\n",
    "    '3 months - 1 year',\n",
    "    '1 - 2 years',\n",
    "    '3 - 5 years',\n",
    "    '5+ years',\n",
    "]\n",
    "\n",
    "sns.countplot(y='How long have you used pandas?',\n",
    "              data=df, order=order,\n",
    "              color='k').set(title=\"How long have you used pandas?\",\n",
    "                             ylabel=\"\");\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "order = [\n",
    "    \"Just looking for now\",\n",
    "    \"Occasionally\",\n",
    "    \"Weekly\",\n",
    "    \"Every day\"\n",
    "]\n",
    "\n",
    "sns.countplot(y='How often do you use Pandas?', data=df, order=order,\n",
    "              color='k').set(title=\"How often do you use Pandas?\",\n",
    "                             ylabel=\"\");\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We included a few questions that were also asked in the [Python Developers Survey](https://www.jetbrains.com/research/python-developers-survey-2018/) so we could compare Pandas' population to Python's. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "90% of our respondents use Python as a primary language (compared with 84% from the PSF survey)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    90.67%\n",
       "No      9.33%\n",
       "Name: Is Python your main language?, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pct_format = \"{:0.2%}\".format\n",
    "\n",
    "df['Is Python your main language?'].str.replace(\"No.*\", \"No\").value_counts(normalize=True).apply(pct_format)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Windows users are well represented (see [Steve Dower's talk](https://www.youtube.com/watch?v=uoI57uMdDD4) on this topic)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linux      61.57%\n",
       "Windows    60.21%\n",
       "MacOS      42.75%\n",
       "Name: What Operating Systems do you use?, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oses = [\n",
    "    \"Linux\",\n",
    "    \"Windows\",\n",
    "    \"MacOS\"\n",
    "]\n",
    "df['What Operating Systems do you use?'].str.split(';').explode().value_counts().div(len(df)).loc[oses].apply(pct_format)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For environment isolation, [conda](https://conda.io/en/latest/) was the most popular."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_and_explode(s):\n",
    "    return s.str.split(\";\").explode().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "replace = {\n",
    "    \"No, I don't isolate environments\": \"No\"\n",
    "}\n",
    "\n",
    "col = 'Do you use any of the following tools to isolate Python environments, if any? (multiple answers)'\n",
    "\n",
    "sns.countplot(y=col,\n",
    "              data=split_and_explode(df[col]).replace(replace),\n",
    "              color='k').set(title='What do you use to isolate Python environments?',\n",
    "                             ylabel=\"\");\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most repondents are Python 3 only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3        92.39%\n",
       "2 & 3     6.80%\n",
       "2         0.81%\n",
       "Name: Python 2 or 3?, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Python 2 or 3?'].value_counts(normalize=True).rename(index={\"2;3\": \"2 & 3\"}).apply(pct_format)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas APIs\n",
    "\n",
    "It can be hard for open source projects to know what features are actually used. We asked a few questions to get an idea."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CSV and Excel are (for better or worse) the most popular formats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='What readers / writers do you use?',\n",
    "              data=df['What readers / writers do you use?'].str.split(';').explode().to_frame(),\n",
    "              color='k').set(title=\"What Readers / Writers do you use?\",\n",
    "                             ylabel=\"\")\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In preperation for a possible refactor of pandas internals, we wanted to get a sense for\n",
    "how common wide (100s of columns or more) DataFrames are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pO8NIktSdYSRJ6s4wkiR1ZxhJkrozjCRJ3RlGkqTuDCNJUneGkSSpO8NIktSdYSRJ6i5V1bsNEyeJO22B8TiWJkJmWuDISJLUnWEkSerOMJIkdWcYSZK6M4wkSd0ZRpKk7gwjSVJ3hpEkqTvDSJLUnWEkSerOMJIkdWcYSZK6M4wkSd0ZRpKk7gwjSVJ3hpEkqTvDSJLUnWEkSerOMJIkdWcYSZK6M4wkSd0ZRpKk7gwjSVJ3hpEkqTvDSJLU3cSEUZJK8s6R+TclObxjkyRJ82Riwgi4HXhhku3WxsaSLFob25EkTVYY3QEcCbxhfEGS7ZN8JsnZ7fGkJBskWZ5km5H1Lkuyw3Trt+WHJzk2yVeBY9dazyRpPTdpZ/8fAC5M8rdj5UcA766qM5M8BDipqnZJ8nngBcBHkjwB+M+quibJJ8bXB3Zpde0K7FtVPxndQJJDgUPvxb5J0nprosKoqm5O8o/A64DRsHg6sGuSqfn7JNkS+CTwVuAjwEFtfmXrA5wwHkRt20cyjMxIUvPWKUnSZIVR8x7gXIaAmbIBsE9V/XR0xSRfBx6RZHvgAOAvVrE+wG33UrslSTOYpHtGAFTVDcBxwCEjxScDr52aSbJ7W7eA44F3AZdU1fUrW1+S1MfEhVHzTmD0W3WvA/ZKcmGSbwO/M7Lsk8DB3HOJblXrS5LWsgyDB82F94wWHo9jaSJkpgWTOjKSJK1DDCNJUneGkSSpO8NIktSdYSRJ6s4wkiR1ZxhJkrozjCRJ3RlGkqTuDCNJUneGkSSpO8NIktSdYSRJ6s4wkiR1ZxhJkrozjCRJ3RlGkqTuDCNJUneGkSSpO8NIktSdYSRJ6s4wkiR1ZxhJkrpb1LsBk2jJkiUsW7asdzMkaZ3hyEiS1J1hJEnqzjCSJHVnGEmSujOMJEndGUaSpO4MI0lSd4aRJKk7w0iS1J1hJEnqzjCSJHVnGEmSujOMJEndGUaSpO5SVb3bMHGSuNM08Xzvq4PMtMCRkSSpO8NIktSdYSRJ6s4wkiR1ZxhJkrozjCRJ3RlGkqTuDCNJUneGkSSpO8NIktSdYSRJ6s4wkiR1ZxhJkrozjCRJ3RlGkqTuDCNJUneGkSSpO8NIktSdYSRJ6s4wkiR1ZxhJkrozjCRJ3RlGkqTuDCNJUnfzEkZJ7kxy/shj8XzUO5+SHJZk85H55Um269kmSdIgVbXmlSS3VtWWK1m+qKruWOMNrYEky4G9quq66ebnWNea7zSps/l470tzlJkW3GuX6ZK8LMkJSU4DTm1lv5/k7CQXJnnbyLp/nOS7Sc5M8k9J3tTKv5Rkrza9XQsQkmyY5P+P1PWqVr60PefTSb6T5OMZvA54IHB6ktPH2vnnSQ4bmX97ktffW/tFkvS/LZqnejZLcn6b/o+qekGb3hN4bFXdkOSZwM7A4xnS8YQkTwFuAw4Cdm/tORc4ZxXbOwS4qar2TrIJ8NUkJ7dlewC7AVcCXwWeVFXvTfJ7wNOmGQl9GPgs8J4kG7S2PH419oEkaTXNVxj9pKp2n6b8lKq6oU0/sz3Oa/NbMoTTVsDxVfVfAElOmMX2ngk8NsmBbX7rVtfPgLOq6opW1/nAYuDMmSqqquVJrk+yB7ADcF5VXT++XpJDgUNn0TZJ0hzNVxjN5LaR6QB/VVUfHF1h9BLZNO7gnkuJm47V9dqqOmmsrqXA7SNFdzK7Ph4NvAz4BYaR0v9SVUcCR7bteLFdkubR2vxq90nAK5JsCZDkQUnuD3wZOCDJZkm2Ap438pzlwJI2feBYXb+bZKNW1yOTbLGK7d/CMAqbzvHA/sDerW5J0lp0b4+M7lZVJyfZBfh6EoBbgYOr6twknwQuAK4Fzh552juA49olsn8dKT+a4fLbuRkq+xFwwCqacCRwYpIrq+ppY237Wftiw41Vdedqd1KStFrm5avd8ynJ4cCtVfWOtbjNDRi+OPGiqrpsFusvrJ0mrYaF9t7XemHtf7V7UiTZFbgcOHU2QSRJmn8LbmQ0CRwZaV3ge18dODKSJC1chpEkqTvDSJLUnWEkSerOMJIkdWcYSZK6M4wkSd0ZRpKk7gwjSVJ3hpEkqTvDSJLUnWEkSerOMJIkdWcYSZK6M4wkSd0ZRpKk7gwjSVJ3hpEkqTvDSJLUnWEkSerOMJIkdWcYSZK6W9S7AZNoyZIlLFu2rHczJGmd4chIktSdYSRJ6s4wkiR1ZxhJkrozjCRJ3RlGkqTuUlW92zBxktwCXNq7HWtoO+C63o1YA5Pefpj8Pkx6+8E+rG3XVdX+0y3w94xWz6VVtVfvRqyJJMsmuQ+T3n6Y/D5MevvBPiwkXqaTJHVnGEmSujOMVs+RvRswDya9D5Pefpj8Pkx6+8E+LBh+gUGS1J0jI0lSd4aRJKk7w2iOkuyf5NIklyf5w97tmUmSDye5NslFI2X3TXJKksvaz21beZK8t/XpwiR79mv53W19cJLTk3w7ycVJXt/KJ6IPSTZNclaSC1r739bKH5rkm62dn0yycSvfpM1f3pYv7tn+UUk2THJeki+0+YnpQ5LlSb6V5Pwky1rZRBxDU5Jsk+TTSb6T5JIkT5y0PsyGYTQHSTYEPgA8C9gVeHGSXfu2akYfBcZ/uewPgVOramfg1DYPQ392bo9Dgb9fS21cmTuAN1bVrsA+wGvavp6UPtwO7FdVjwN2B/ZPsg/wN8C7q+oRwI+BQ9r6hwA/buXvbustFK8HLhmZn7Q+PK2qdh/5XZxJOYamHAGcWFWPBh7H8FpMWh9Wrap8zPIBPBE4aWT+LcBberdrJe1dDFw0Mn8p8IA2/QCGX94F+CDw4unWWygP4PPAMyaxD8DmwLnAExh+U37R+PEEnAQ8sU0vautlAbR9R4YPu/2ALwCZpD4Ay4Htxsom5hgCtgb+Y3w/TlIfZvtwZDQ3DwJWjMxf0comxQ5VdVWbvhrYoU0v6H61yz17AN9kgvrQLm+dD1wLnAJ8D7ixqu5oq4y28e72t+U3Afdbuy2e1nuANwN3tfn7MVl9KODkJOckObSVTcwxBDwU+BHwkXap9OgkWzBZfZgVw2g9VcNp04L/Xn+SLYHPAIdV1c2jyxZ6H6rqzqranWF08Xjg0Z2bNCdJngtcW1Xn9G7LGti3qvZkuHz1miRPGV240I8hhhHmnsDfV9UewG3cc0kOmIg+zIphNDc/BB48Mr9jK5sU1yR5AED7eW0rX5D9SrIRQxB9vKo+24onqg8AVXUjcDrDJa1tkkz9TcjRNt7d/rZ8a+D6tdzUcU8Cnp9kOfDPDJfqjmCC+lBVP2w/rwWOZzgpmKRj6Argiqr6Zpv/NEM4TVIfZsUwmpuzgZ3bt4k2Bg4CTujcprk4AXhpm34pw32YqfKXtG/i7APcNHIJoIskAT4EXFJV7xpZNBF9SLJ9km3a9GYM97suYQilA9tq4+2f6teBwGntjLebqnpLVe1YVYsZjvXTquo3mZA+JNkiyVZT08AzgYuYkGMIoKquBlYkeVQr+iXg20xQH2at902rSXsAzwa+y3D9/497t2cl7fwn4Crg5wxnV4cwXL8/FbgM+Hfgvm3dMHxL8HvAt4C9FkD792W49HAhcH57PHtS+gA8Fjivtf8i4K2t/GHAWcDlwKeATVr5pm3+8rb8Yb1fg7H+LAW+MEl9aO28oD0unnq/TsoxNNKP3YFl7Vj6HLDtpPVhNg//HJAkqTsv00mSujOMJEndGUaSpO4MI0lSd4aRJKk7w0haTyU5LMnmvdshgf/pVVpvtb+ssFdVXde7LZIjI2kBS/KS9n9pLkhybJLFSU5rZacmeUhb76NJDhx53q3t59IkXxr5fzgfb7+d/zrggcDpSU7v0zvpHotWvYqkHpLsBvwJ8ItVdV2S+wLHAMdU1TFJXgG8FzhgFVXtAewGXAl8FXhSVb03ye8x/K8fR0bqzpGRtHDtB3xqKiyq6gaGP7b6ibb8WIY/m7QqZ1XVFVV1F8OfVVp8L7RVWiOGkbRuuIP2fk6yAbDxyLLbR6bvxCsiWoAMI2nhOg14UZL7AbTLdF9j+AvaAL8JfKVNLweWtOnnAxvNov5bgK3mq7HSmvAMSVqgquriJG8HzkhyJ8NfAX8tw3/9/H2G/wD68rb6UcDnk1wAnMjwT9hW5UjgxCRXVtXT5r8H0uz51W5JUndeppMkdWcYSZK6M4wkSd0ZRpKk7gwjSVJ3hpEkqTvDSJLU3X8DJOWCrawh3sAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='How often do you work with \"wide\" DataFrames (100s of columns or more)',\n",
    "              data=df, color='k').set(title=\"How often do you use 'wide' DataFrames?\",\n",
    "                                      ylabel=\"\");\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas is slowly growing new exentension types. Categoricals are the most popular,\n",
    "and the nullable integer type is already almost as popular as datetime with timezone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='Which extension types do you use?',\n",
    "              data=split_and_explode(df['Which extension types do you use?']),\n",
    "              color='k').set(title=\"Which extension types do you use?\",\n",
    "                             ylabel=\"\")\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More and better examples seem to be a high-priority development item.\n",
    "Pandas recently received a NumFOCUS grant to improve our documentation,\n",
    "which we're using to write tutorial-style documentation, which should help\n",
    "meet this need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='Which would help you most right now?',\n",
    "              data=df,\n",
    "              color='k').set(title=\"Which would help you most right now?\",\n",
    "                             ylabel=\"\")\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also asked about specific, commonly-requested features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x864 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "common = (df[df.columns[df.columns.str.startswith(\"What common feature\")]]\n",
    "          .rename(columns=lambda x: x.lstrip(\"What common feature requests do you care about most?[\").rstrip(r\"]\")))\n",
    "\n",
    "counts = (\n",
    "    common.apply(pd.value_counts)\n",
    "    .T.stack().reset_index()\n",
    "    .rename(columns={'level_0': 'Question', 'level_1': \"Importance\", 0: \"count\"})\n",
    ")\n",
    "\n",
    "order = [\"Not relevant for me\", \"Somewhat useful\", 'Critical to me']\n",
    "g = (\n",
    "    sns.FacetGrid(counts, col=\"Question\", col_wrap=2, aspect=1.5, sharex=False, height=3)\n",
    "    .map(sns.barplot, \"Importance\", \"count\", order=order)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of these, the clear standout is \"scaling\" to large datasets. A couple observations:\n",
    "\n",
    "1. Perhaps pandas' documentation should do a better job of promoting libraries that provide scalable dataframes (like [Dask](https://dask.org), [vaex](https://dask.org), and [modin](https://modin.readthedocs.io/en/latest/))\n",
    "2. Memory efficiency (perhaps from a native string data type, fewer internal copies, etc.) is a valuable goal.\n",
    "\n",
    "After that, the next-most critical improvement is integer missing values. Those were actually added in [Pandas 0.24](https://pandas.pydata.org/pandas-docs/stable/whatsnew/v0.24.0.html#optional-integer-na-support), but they're not the default, and there's still some incompatibilites with the rest of pandas API."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas is a less conservative library than, say, NumPy. We're approaching 1.0, but on the way we've made many deprecations and some outright API breaking changes. Fortunately, most people are OK with the tradeoff."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    94.89%\n",
       "No      5.11%\n",
       "Name: Is Pandas stable enough for you?, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Is Pandas stable enough for you?'].value_counts(normalize=True).apply(pct_format)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's a perception (which is shared by many of the pandas maintainers) that the pandas API is too large. To measure that, we asked whether users thought that pandas' API was too large, too small, or just right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "renamer = {\"Generally speaking, do you think pandas' API is too large (hard to remember / find the method you want) or too small (you wish it had more features)?\": \"Is the pandas API too large?\"}\n",
    "sns.countplot(y=\"Is the pandas API too large?\",\n",
    "              data=df.rename(columns=renamer),\n",
    "              color='k').set(title=\"Is the pandas API too large?\",\n",
    "                             ylabel=\"\")\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we asked for an overall satisfaction with the library, from 1 (not very unsatisfied) to 5 (very satisfied)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='How satisfied are you with pandas?',\n",
    "              data=df[['How satisfied are you with pandas?']].dropna().astype(int),\n",
    "              color='k').set(title=\"How satisfied are you with pandas?\",\n",
    "                             ylabel=\"\")\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most people are very satisfied. The average response is 4.39. I look forward to tracking this number over time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you're analyzing the raw data, be sure to share the results with us [@pandas_dev](https://twitter.com/pandas_dev)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}