Pandas_basics.ipynb

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   "source": [
    "## Getting Started with Pandas"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd "
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Series"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "labels = ['a', 'b', 'c']\n",
    "my_data = [13,20,45]\n",
    "arr = np.array(my_data)\n",
    "d = {'a':10, 'b':20, 'c':45}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# series from a list\n",
    "pd.Series(my_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# map labels \n",
    "pd.Series(my_data, index=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# map labels \n",
    "pd.Series(arr,labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# series from a dict \n",
    "pd.Series(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# series from lists\n",
    "ser1 = pd.Series([8,3,2,5,6], ['KE','UG', 'TZ', 'ETH','SML'])\n",
    "ser1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# access values in a list\n",
    "ser1['TZ']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.random import randn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataframe\n",
    "df = pd.DataFrame(randn(5,4),['A','B','C','D','E'],\n",
    "                  ['col1','col2','col3','col4'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# access column \n",
    "\n",
    "df['col3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "type(df['col3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# access column values\n",
    "df.col3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# access list of columns\n",
    "df[['col3', 'col1']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#add new column from existing\n",
    "\n",
    "df['new'] = df['col3'] + df['col2']\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop column\n",
    "df.drop('new', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop rows\n",
    "df.drop('C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Access ROWS by location(loc)\n",
    "\n",
    "df.loc['B']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Access rows via index based location (iloc)\n",
    "df.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select item from dataframe\n",
    "\n",
    "df.iloc[]"
   ]
  }
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