{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Tools - pandas**\n",
"\n",
"*The `pandas` library provides high-performance, easy-to-use data structures and data analysis tools. The main data structure is the `DataFrame`, which you can think of as an in-memory 2D table (like a spreadsheet, with column names and row labels). Many features available in Excel are available programmatically, such as creating pivot tables, computing columns based on other columns, plotting graphs, etc. You can also group rows by column value, or join tables much like in SQL. Pandas is also great at handling time series.*\n",
"\n",
"Prerequisites:\n",
"* NumPy – if you are not familiar with NumPy, we recommend that you go through the [NumPy tutorial](tools_numpy.ipynb) now."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
" \n",
" ![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\") \n",
" | \n",
" \n",
" ![](\"https://kaggle.com/static/images/open-in-kaggle.svg\") \n",
" | \n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Setup"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, let's import `pandas`. People usually import it as `pd`:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# `Series` objects\n",
"The `pandas` library contains the following useful data structures:\n",
"* `Series` objects, that we will discuss now. A `Series` object is 1D array, similar to a column in a spreadsheet (with a column name and row labels).\n",
"* `DataFrame` objects. This is a 2D table, similar to a spreadsheet (with column names and row labels).\n",
"* `Panel` objects. You can see a `Panel` as a dictionary of `DataFrame`s. These are less used, so we will not discuss them here."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Creating a `Series`\n",
"Let's start by creating our first `Series` object!"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 2\n",
"1 -1\n",
"2 3\n",
"3 5\n",
"dtype: int64"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = pd.Series([2,-1,3,5])\n",
"s"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Similar to a 1D `ndarray`\n",
"`Series` objects behave much like one-dimensional NumPy `ndarray`s, and you can often pass them as parameters to NumPy functions:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 7.389056\n",
"1 0.367879\n",
"2 20.085537\n",
"3 148.413159\n",
"dtype: float64"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"np.exp(s)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Arithmetic operations on `Series` are also possible, and they apply *elementwise*, just like for `ndarray`s:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1002\n",
"1 1999\n",
"2 3003\n",
"3 4005\n",
"dtype: int64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s + [1000,2000,3000,4000]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similar to NumPy, if you add a single number to a `Series`, that number is added to all items in the `Series`. This is called * broadcasting*:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1002\n",
"1 999\n",
"2 1003\n",
"3 1005\n",
"dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s + 1000"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The same is true for all binary operations such as `*` or `/`, and even conditional operations:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 False\n",
"1 True\n",
"2 False\n",
"3 False\n",
"dtype: bool"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s < 0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Index labels\n",
"Each item in a `Series` object has a unique identifier called the *index label*. By default, it is simply the rank of the item in the `Series` (starting from `0`) but you can also set the index labels manually:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"alice 68\n",
"bob 83\n",
"charles 112\n",
"darwin 68\n",
"dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s2 = pd.Series([68, 83, 112, 68], index=[\"alice\", \"bob\", \"charles\", \"darwin\"])\n",
"s2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can then use the `Series` just like a `dict`:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"83"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s2[\"bob\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To make it clear when you are accessing by label or by integer location, it is recommended to always use the `loc` attribute when accessing by label, and the `iloc` attribute when accessing by integer location:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"83"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s2.loc[\"bob\"]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"83"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s2.iloc[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Slicing a `Series` also slices the index labels:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"bob 83\n",
"charles 112\n",
"dtype: int64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s2.iloc[1:3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This can lead to unexpected results when using the default numeric labels, so be careful:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1000\n",
"1 1001\n",
"2 1002\n",
"3 1003\n",
"dtype: int64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"surprise = pd.Series([1000, 1001, 1002, 1003])\n",
"surprise"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2 1002\n",
"3 1003\n",
"dtype: int64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"surprise_slice = surprise[2:]\n",
"surprise_slice"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Oh, look! The first element has index label `2`. The element with index label `0` is absent from the slice:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Key error: 0\n"
]
}
],
"source": [
"try:\n",
" surprise_slice[0]\n",
"except KeyError as e:\n",
" print(\"Key error:\", e)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But remember that you can access elements by integer location using the `iloc` attribute. This illustrates another reason why it's always better to use `loc` and `iloc` to access `Series` objects:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1002"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"surprise_slice.iloc[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Init from `dict`\n",
"You can create a `Series` object from a `dict`. The keys will be used as index labels:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"alice 68\n",
"bob 83\n",
"colin 86\n",
"darwin 68\n",
"dtype: int64"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weights = {\"alice\": 68, \"bob\": 83, \"colin\": 86, \"darwin\": 68}\n",
"s3 = pd.Series(weights)\n",
"s3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can control which elements you want to include in the `Series` and in what order by explicitly specifying the desired `index`:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"colin 86\n",
"alice 68\n",
"dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s4 = pd.Series(weights, index = [\"colin\", \"alice\"])\n",
"s4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Automatic alignment\n",
"When an operation involves multiple `Series` objects, `pandas` automatically aligns items by matching index labels."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['alice', 'bob', 'charles', 'darwin'], dtype='object')\n",
"Index(['alice', 'bob', 'colin', 'darwin'], dtype='object')\n"
]
},
{
"data": {
"text/plain": [
"alice 136.0\n",
"bob 166.0\n",
"charles NaN\n",
"colin NaN\n",
"darwin 136.0\n",
"dtype: float64"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(s2.keys())\n",
"print(s3.keys())\n",
"\n",
"s2 + s3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The resulting `Series` contains the union of index labels from `s2` and `s3`. Since `\"colin\"` is missing from `s2` and `\"charles\"` is missing from `s3`, these items have a `NaN` result value (i.e. Not-a-Number means *missing*).\n",
"\n",
"Automatic alignment is very handy when working with data that may come from various sources with varying structure and missing items. But if you forget to set the right index labels, you can have surprising results:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"s2 = [ 68 83 112 68]\n",
"s5 = [1000 1000 1000 1000]\n"
]
},
{
"data": {
"text/plain": [
"alice NaN\n",
"bob NaN\n",
"charles NaN\n",
"darwin NaN\n",
"0 NaN\n",
"1 NaN\n",
"2 NaN\n",
"3 NaN\n",
"dtype: float64"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s5 = pd.Series([1000,1000,1000,1000])\n",
"print(\"s2 =\", s2.values)\n",
"print(\"s5 =\", s5.values)\n",
"\n",
"s2 + s5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pandas could not align the `Series`, since their labels do not match at all, hence the full `NaN` result."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Init with a scalar\n",
"You can also initialize a `Series` object using a scalar and a list of index labels: all items will be set to the scalar."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"life 42\n",
"universe 42\n",
"everything 42\n",
"dtype: int64"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"meaning = pd.Series(42, [\"life\", \"universe\", \"everything\"])\n",
"meaning"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## `Series` name\n",
"A `Series` can have a `name`:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"bob 83\n",
"alice 68\n",
"Name: weights, dtype: int64"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s6 = pd.Series([83, 68], index=[\"bob\", \"alice\"], name=\"weights\")\n",
"s6"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting a `Series`\n",
"Pandas makes it easy to plot `Series` data using matplotlib (for more details on matplotlib, check out the [matplotlib tutorial](tools_matplotlib.ipynb)). Just import matplotlib and call the `plot()` method:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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69P50S03kZ7O+YrcmFzsklbuIhIXEuBgemDSMzbv2cefrmlzsUFTuIhI2RnRt\nwzXH9eLlBRt4Z4kmF/s+KncRCSvXHd+bQZ1b86tXv2ZrabnXcUKWyl1Ewkqs38eDE4exp6Kaq2cs\nYMlGjX8/GJW7iISd3umtuPfswazaUsq4//mcHz09j7zCEq9jhRTzakrN7Oxsl5ub68m2RSQy7NpX\nxfQvC3jqi3x2lFUxpkdbrh3biyN6tsXMvI7XJMwszzmXfcjlVO4iEu72VlTz/Lx1TP10LVtLKxjW\nJYVrj+vF8f3bR1zJq9xFJOqUV9XwUt4GHv3kGzbs2Ee/Dq245rhenDa4I35fZJR8sOUe9DF3M/Ob\n2UIze/Mg78Wb2UwzW2Nmc80ss35xRUQOX0Ksn4tGd+Pjm4/lz+cOpaqmlp8+v5ATH/iEF3PXU1VT\n63XEZlOfE6rXA8u/473LgR3OuV7Ag8C9hxtMRKShYv0+zs7K4L0bf8BfLxhBfKyfn7+0mGPv+zfP\nzC6IignIgip3M8sATgee+I5FJgDTAo9fAo63SDvQJSJhx+8zTh/SkbeuO4qnLssmvXU8v31tKUf/\n6WMe/3QteyuqvY7YZILdc38I+AXwXX/TdAbWAzjnqoFdQNsDFzKzKWaWa2a5xcXFDYgrIlJ/ZsbY\nfum8fPURPHfFKHq3b8kf3lrOkfd+xF8+XM2ufZE3V80hy93MxgFbnXN5h7sx59xU51y2cy47LS3t\ncFcnIlIvZsYRPdvx3BWjeeUnR5DVtQ0PvL+KI+/5iHvfWcG2PRVeR2w0wey5HwmMN7MC4AVgrJnN\nOGCZjUAXADOLAZIBzcspIiFrRNc2PHnZSN667mh+0DeNRz/5hqPu/Yg731jK5l37vI532Oo1FNLM\njgVuds6NO+D1a4DBzrmrzOw84Czn3MTvW5eGQopIKPmmeA9///c3/HPhRszgnKwMrvpBT7q1TfI6\n2n9p9KGQB9nAXWY2PvD0SaCtma0BbgJuaeh6RUS80DOtJfefO5SPbz6WSSO78PKCjRx3/7+54YWF\nrC4q9TpevekiJhGRg9i6u5zHP1vLs3PXUVZZwykDO3Dt2F4M6pzsaS5doSoi0gh27K3k6S/yefrL\nAkrLq/lBnzSuHduLkZmpnuRp8sMyIiLRoE1SHDed1JcvbhnLz0/uy5KNuzj30dk88N5KvNo5DobK\nXUQkCK0TYrnmuF58/suxTMzO4C8freHut5aHbMHHeB1ARCSctIjzc89ZQ2gR6+fxz/Ipr6rlzvED\n8YXYxGQqdxGRevL5jDvGDyQh1s9jn66lvKqGe84eElIzT6rcRUQawMy45dR+JMT6efjD1ZRX1/LA\nxKHE+kPjaLfKXUSkgcyMG09bfL6QAAAHHUlEQVTsQ0Ksn3vfWUFldQ1/OX848TF+r6PphKqIyOG6\n+tie3HHGAN5dWsSVz+SFxJTCKncRkUZw2ZHd+eNZg/lkVTE/enq+59MJq9xFRBrJ+TldeWDiUObm\nb+fSp+axu9y7qYRV7iIijeiHwzN45IIRLFq/k4uemMvOskpPcqjcRUQa2WmDOzL1kixWbCnlvKlz\nPJknXuUuItIExvZL56lLR1KwfS+THptN0e7yZt2+yl1EpIkc1bsd0388ii27ypn42Gw27Chrtm2r\n3EVEmlBO91RmTB7Fjr2VTHx0NgXb9jbLdlXuIiJNbHjXNjw/ZTTl1bVMfGx2s9z8Q+UuItIMBnZK\n5oUpo3HApKlzWLppV5NuT+UuItJM+qS3YtaVY0iI8XH+1DksWr+zybalchcRaUbd2yUx88oxJCfG\nctETc5lfUNIk21G5i4g0sy6pibx45RG0bx3PJU/O44s12xp9Gyp3EREPdEhOYOaUMXRrm8iP/jGf\nj1dsbdT1q9xFRDyS1iqe568YTZ/0lkx5Jpd3lmxutHWr3EVEPNQmKY5nJ49mcOdkrnluIa8t2tgo\n61W5i4h4LLlFLM9cPoqRmW24YeYiZs1ff9jrVLmLiISApPgYnr4sh6N7p/GLlxczfXbBYa1P5S4i\nEiJaxPl5/JIsThyQzm2vLWXqp980eF0qdxGREBIf4+dvF45g3JCO3P3WCh7+YDXOuXqv55DlbmYJ\nZjbPzL4ys6VmdudBlrnMzIrNbFHga3K9k4iICACxfh8Pnzecc7IyePCDVdz7zsp6F3xMEMtUAGOd\nc3vMLBb43Mzeds7NOWC5mc65a+u1dREROSi/z/jT2UNIiPXx6CffUF5Vw23jBgT984csd1f362JP\n4Gls4Kv+fyOIiEi9+HzG7yYMIj7Gz5Of51NeVRP0zwaz546Z+YE8oBfwV+fc3IMsdraZHQOsAm50\nzh3+WB4RkShnZvzm9P60iPXzyMdrgv65oE6oOudqnHPDgAwgx8wGHbDIG0Cmc24I8D4w7TtCTjGz\nXDPLLS4uDjqkiEg0MzNuPrkvN5/UJ/ifqe9BejO7DShzzt3/He/7gRLnXPL3rSc7O9vl5ubWa9si\nItHOzPKcc9mHWi6Y0TJpZpYSeNwCOBFYccAyHfd7Oh5YXr+4IiLSmII55t4RmBbYI/cBs5xzb5rZ\nXUCuc+514DozGw9UAyXAZU0VWEREDq3eh2Uaiw7LiIjUX6MdlhERkfCjchcRiUAqdxGRCKRyFxGJ\nQCp3EZEI5NloGTMrBVZ6snHvtAMa/zbnoU2fOTroMzefbs65tEMtFNTcMk1kZTDDeSKJmeXqM0c+\nfeboEOqfWYdlREQikMpdRCQCeVnuUz3ctlf0maODPnN0COnP7NkJVRERaTo6LCMiEoE8KXczO8XM\nVprZGjO7xYsMzcnMupjZx2a2LHCT8eu9ztQczMxvZgvN7E2vszQHM0sxs5fMbIWZLTezMV5nampm\ndmPg3/QSM3vezBK8ztTYzOwpM9tqZkv2ey3VzN43s9WB7228zHgwzV7ugamD/wqcCgwAzjez4O/6\nGp6qgZ855wYAo4FrouAzA1xPdM3t/zDwjnOuHzCUCP/sZtYZuA7Ids4NAvzAed6mahL/AE454LVb\ngA+dc72BDwPPQ4oXe+45wBrn3FrnXCXwAjDBgxzNxjm32Tm3IPC4lLr/6Tt7m6ppmVkGcDrwhNdZ\nmoOZJQPHAE8COOcqnXM7vU3VLGKAFmYWAyQCmzzO0+icc59Sd5+K/U3g/24nOg04s1lDBcGLcu8M\n7H/z7A1EeNHtz8wygeHAwW4yHkkeAn4B1HodpJl0B4qBpwOHop4wsySvQzUl59xG4H5gHbAZ2OWc\ne8/bVM0m3Tm3OfB4C5DuZZiD0QnVZmRmLYGXgRucc7u9ztNUzGwcsNU5l+d1lmYUA4wA/u6cGw7s\nJQT/VG9MgePME6j7xdYJSDKzi7xN1fxc3ZDDkBt26EW5bwS67Pc8I/BaRDOzWOqK/Vnn3Cte52li\nRwLjzayAusNuY81shreRmtwGYINz7j9/kb1EXdlHshOAfOdcsXOuCngFOMLjTM2l6D/3jg583+px\nnv/Hi3KfD/Q2s+5mFkfdCZjXPcjRbMzMqDsWu9w594DXeZqac+5W51yGcy6Tuv++HznnInqPzjm3\nBVhvZn0DLx0PLPMwUnNYB4w2s8TAv/HjifCTyPt5Hbg08PhS4DUPsxxUs08c5pyrNrNrgXepO7v+\nlHNuaXPnaGZHAhcDX5vZosBrv3LOveVhJml8PwWeDey0rAV+5HGeJuWcm2tmLwELqBsRtpAQv2qz\nIczseeBYoJ2ZbQBuB+4BZpnZ5UAhMNG7hAenK1RFRCKQTqiKiEQglbuISARSuYuIRCCVu4hIBFK5\ni4hEIJW7iEgEUrmLiEQglbuISAT6X/s00irHqiZcAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"temperatures = [4.4,5.1,6.1,6.2,6.1,6.1,5.7,5.2,4.7,4.1,3.9,3.5]\n",
"s7 = pd.Series(temperatures, name=\"Temperature\")\n",
"s7.plot()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are *many* options for plotting your data. It is not necessary to list them all here: if you need a particular type of plot (histograms, pie charts, etc.), just look for it in the excellent [Visualization](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html) section of pandas' documentation, and look at the example code."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Handling time\n",
"Many datasets have timestamps, and pandas is awesome at manipulating such data:\n",
"* it can represent periods (such as 2016Q3) and frequencies (such as \"monthly\"),\n",
"* it can convert periods to actual timestamps, and *vice versa*,\n",
"* it can resample data and aggregate values any way you like,\n",
"* it can handle timezones.\n",
"\n",
"## Time range\n",
"Let's start by creating a time series using `pd.date_range()`. It returns a `DatetimeIndex` containing one datetime per hour for 12 hours starting on October 29th 2016 at 5:30pm."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DatetimeIndex(['2016-10-29 17:30:00', '2016-10-29 18:30:00',\n",
" '2016-10-29 19:30:00', '2016-10-29 20:30:00',\n",
" '2016-10-29 21:30:00', '2016-10-29 22:30:00',\n",
" '2016-10-29 23:30:00', '2016-10-30 00:30:00',\n",
" '2016-10-30 01:30:00', '2016-10-30 02:30:00',\n",
" '2016-10-30 03:30:00', '2016-10-30 04:30:00'],\n",
" dtype='datetime64[ns]', freq='H')"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dates = pd.date_range('2016/10/29 5:30pm', periods=12, freq='h')\n",
"dates"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This `DatetimeIndex` may be used as an index in a `Series`:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2016-10-29 17:30:00 4.4\n",
"2016-10-29 18:30:00 5.1\n",
"2016-10-29 19:30:00 6.1\n",
"2016-10-29 20:30:00 6.2\n",
"2016-10-29 21:30:00 6.1\n",
"2016-10-29 22:30:00 6.1\n",
"2016-10-29 23:30:00 5.7\n",
"2016-10-30 00:30:00 5.2\n",
"2016-10-30 01:30:00 4.7\n",
"2016-10-30 02:30:00 4.1\n",
"2016-10-30 03:30:00 3.9\n",
"2016-10-30 04:30:00 3.5\n",
"Freq: H, dtype: float64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp_series = pd.Series(temperatures, dates)\n",
"temp_series"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's plot this series:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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D0mURcU/FOT0/1ZZU+afaZWaS2sqsVteXOi/1eJa1vZXiVdsAEMCdwH8Ap9XwfDgA+CDw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\njBu3mVlm3LjNzDLz/wEJ5lbocofr5wAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"temp_series.plot(kind=\"bar\")\n",
"\n",
"plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Resampling\n",
"Pandas lets us resample a time series very simply. Just call the `resample()` method and specify a new frequency:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp_series_freq_2h = temp_series.resample(\"2h\")\n",
"temp_series_freq_2h"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The resampling operation is actually a deferred operation, which is why we did not get a `Series` object, but a `DatetimeIndexResampler` object instead. To actually perform the resampling operation, we can simply call the `mean()` method. Pandas will compute the mean of every pair of consecutive hours:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"temp_series_freq_2h = temp_series_freq_2h.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's plot the result:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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f9P8vzwD2Ah4xWqrhqufP+BlX5fzNZ2/25KSks4HX275kwbI1dE/v/g+2dxkt\n3ACS9gFu84LriBese7ztfxgh1mAzkD/jZ0SV81fI3nJxN7/xol0ZPzHLmi3uiIhYWsvHuCMiYgkp\n7oiIYlLcERHFpLgjIor5/4Xj0noYGwK1AAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"temp_series_freq_2h.plot(kind=\"bar\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note how the values have automatically been aggregated into 2-hour periods. If we look at the 6-8pm period, for example, we had a value of `5.1` at 6:30pm, and `6.1` at 7:30pm. After resampling, we just have one value of `5.6`, which is the mean of `5.1` and `6.1`. Rather than computing the mean, we could have used any other aggregation function, for example we can decide to keep the minimum value of each period:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2016-10-29 16:00:00 4.4\n",
"2016-10-29 18:00:00 5.1\n",
"2016-10-29 20:00:00 6.1\n",
"2016-10-29 22:00:00 5.7\n",
"2016-10-30 00:00:00 4.7\n",
"2016-10-30 02:00:00 3.9\n",
"2016-10-30 04:00:00 3.5\n",
"Freq: 2H, dtype: float64"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp_series_freq_2h = temp_series.resample(\"2h\").min()\n",
"temp_series_freq_2h"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or, equivalently, we could use the `apply()` method instead:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2016-10-29 16:00:00 4.4\n",
"2016-10-29 18:00:00 5.1\n",
"2016-10-29 20:00:00 6.1\n",
"2016-10-29 22:00:00 5.7\n",
"2016-10-30 00:00:00 4.7\n",
"2016-10-30 02:00:00 3.9\n",
"2016-10-30 04:00:00 3.5\n",
"Freq: 2H, dtype: float64"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp_series_freq_2h = temp_series.resample(\"2h\").apply(\"min\")\n",
"temp_series_freq_2h"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Upsampling and interpolation\n",
"It was an example of downsampling. We can also upsample (i.e. increase the frequency), but it will create holes in our data:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2016-10-29 17:30:00 4.4\n",
"2016-10-29 17:45:00 NaN\n",
"2016-10-29 18:00:00 NaN\n",
"2016-10-29 18:15:00 NaN\n",
"2016-10-29 18:30:00 5.1\n",
"2016-10-29 18:45:00 NaN\n",
"2016-10-29 19:00:00 NaN\n",
"2016-10-29 19:15:00 NaN\n",
"2016-10-29 19:30:00 6.1\n",
"2016-10-29 19:45:00 NaN\n",
"Freq: 15T, dtype: float64"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp_series_freq_15min = temp_series.resample(\"15Min\").mean()\n",
"temp_series_freq_15min.head(n=10) # `head` displays the top n values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One solution is to fill the gaps by interpolating. We just call the `interpolate()` method. The default is to use linear interpolation, but we can also select another method, such as cubic interpolation:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"2016-10-29 17:30:00 4.400000\n",
"2016-10-29 17:45:00 4.452911\n",
"2016-10-29 18:00:00 4.605113\n",
"2016-10-29 18:15:00 4.829758\n",
"2016-10-29 18:30:00 5.100000\n",
"2016-10-29 18:45:00 5.388992\n",
"2016-10-29 19:00:00 5.669887\n",
"2016-10-29 19:15:00 5.915839\n",
"2016-10-29 19:30:00 6.100000\n",
"2016-10-29 19:45:00 6.203621\n",
"Freq: 15T, dtype: float64"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp_series_freq_15min = temp_series.resample(\"15Min\").interpolate(method=\"cubic\")\n",
"temp_series_freq_15min.head(n=10)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
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+Z0zfAPQ0xux2Pt8GtDHGHCpgWUOBoQDVqlVr+ddffxUqJ6WUOq8j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\nXymlfMj/A19hNXiBHe8QAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"temp_series.plot(label=\"Period: 1 hour\")\n",
"temp_series_freq_15min.plot(label=\"Period: 15 minutes\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Timezones\n",
"By default, datetimes are *naive*: they are not aware of timezones, so 2016-10-30 02:30 might mean October 30th 2016 at 2:30am in Paris or in New York. We can make datetimes timezone *aware* by calling the `tz_localize()` method:"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2016-10-29 17:30:00-04:00 4.4\n",
"2016-10-29 18:30:00-04:00 5.1\n",
"2016-10-29 19:30:00-04:00 6.1\n",
"2016-10-29 20:30:00-04:00 6.2\n",
"2016-10-29 21:30:00-04:00 6.1\n",
"2016-10-29 22:30:00-04:00 6.1\n",
"2016-10-29 23:30:00-04:00 5.7\n",
"2016-10-30 00:30:00-04:00 5.2\n",
"2016-10-30 01:30:00-04:00 4.7\n",
"2016-10-30 02:30:00-04:00 4.1\n",
"2016-10-30 03:30:00-04:00 3.9\n",
"2016-10-30 04:30:00-04:00 3.5\n",
"dtype: float64"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp_series_ny = temp_series.tz_localize(\"America/New_York\")\n",
"temp_series_ny"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that `-04:00` is now appended to all the datetimes. It means that these datetimes refer to [UTC](https://en.wikipedia.org/wiki/Coordinated_Universal_Time) - 4 hours.\n",
"\n",
"We can convert these datetimes to Paris time like this:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2016-10-29 23:30:00+02:00 4.4\n",
"2016-10-30 00:30:00+02:00 5.1\n",
"2016-10-30 01:30:00+02:00 6.1\n",
"2016-10-30 02:30:00+02:00 6.2\n",
"2016-10-30 02:30:00+01:00 6.1\n",
"2016-10-30 03:30:00+01:00 6.1\n",
"2016-10-30 04:30:00+01:00 5.7\n",
"2016-10-30 05:30:00+01:00 5.2\n",
"2016-10-30 06:30:00+01:00 4.7\n",
"2016-10-30 07:30:00+01:00 4.1\n",
"2016-10-30 08:30:00+01:00 3.9\n",
"2016-10-30 09:30:00+01:00 3.5\n",
"dtype: float64"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp_series_paris = temp_series_ny.tz_convert(\"Europe/Paris\")\n",
"temp_series_paris"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You may have noticed that the UTC offset changes from `+02:00` to `+01:00`: this is because France switches to winter time at 3am that particular night (time goes back to 2am). Notice that 2:30am occurs twice! Let's go back to a naive representation (if you log some data hourly using local time, without storing the timezone, you might get something like this):"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2016-10-29 23:30:00 4.4\n",
"2016-10-30 00:30:00 5.1\n",
"2016-10-30 01:30:00 6.1\n",
"2016-10-30 02:30:00 6.2\n",
"2016-10-30 02:30:00 6.1\n",
"2016-10-30 03:30:00 6.1\n",
"2016-10-30 04:30:00 5.7\n",
"2016-10-30 05:30:00 5.2\n",
"2016-10-30 06:30:00 4.7\n",
"2016-10-30 07:30:00 4.1\n",
"2016-10-30 08:30:00 3.9\n",
"2016-10-30 09:30:00 3.5\n",
"dtype: float64"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp_series_paris_naive = temp_series_paris.tz_localize(None)\n",
"temp_series_paris_naive"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now `02:30` is really ambiguous. If we try to localize these naive datetimes to the Paris timezone, we get an error:"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Cannot infer dst time from 2016-10-30 02:30:00, try using the 'ambiguous' argument\n"
]
}
],
"source": [
"try:\n",
" temp_series_paris_naive.tz_localize(\"Europe/Paris\")\n",
"except Exception as e:\n",
" print(type(e))\n",
" print(e)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fortunately, by using the `ambiguous` argument we can tell pandas to infer the right DST (Daylight Saving Time) based on the order of the ambiguous timestamps:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2016-10-29 23:30:00+02:00 4.4\n",
"2016-10-30 00:30:00+02:00 5.1\n",
"2016-10-30 01:30:00+02:00 6.1\n",
"2016-10-30 02:30:00+02:00 6.2\n",
"2016-10-30 02:30:00+01:00 6.1\n",
"2016-10-30 03:30:00+01:00 6.1\n",
"2016-10-30 04:30:00+01:00 5.7\n",
"2016-10-30 05:30:00+01:00 5.2\n",
"2016-10-30 06:30:00+01:00 4.7\n",
"2016-10-30 07:30:00+01:00 4.1\n",
"2016-10-30 08:30:00+01:00 3.9\n",
"2016-10-30 09:30:00+01:00 3.5\n",
"dtype: float64"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp_series_paris_naive.tz_localize(\"Europe/Paris\", ambiguous=\"infer\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Periods\n",
"The `pd.period_range()` function returns a `PeriodIndex` instead of a `DatetimeIndex`. For example, let's get all quarters in 2016 and 2017:"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PeriodIndex(['2016Q1', '2016Q2', '2016Q3', '2016Q4', '2017Q1', '2017Q2',\n",
" '2017Q3', '2017Q4'],\n",
" dtype='period[Q-DEC]')"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"quarters = pd.period_range('2016Q1', periods=8, freq='Q')\n",
"quarters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Adding a number `N` to a `PeriodIndex` shifts the periods by `N` times the `PeriodIndex`'s frequency:"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PeriodIndex(['2016Q4', '2017Q1', '2017Q2', '2017Q3', '2017Q4', '2018Q1',\n",
" '2018Q2', '2018Q3'],\n",
" dtype='period[Q-DEC]')"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"quarters + 3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `asfreq()` method lets us change the frequency of the `PeriodIndex`. All periods are lengthened or shortened accordingly. For example, let's convert all the quarterly periods to monthly periods (zooming in):"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PeriodIndex(['2016-03', '2016-06', '2016-09', '2016-12', '2017-03', '2017-06',\n",
" '2017-09', '2017-12'],\n",
" dtype='period[M]')"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"quarters.asfreq(\"M\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By default, the `asfreq` zooms on the end of each period. We can tell it to zoom on the start of each period instead:"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PeriodIndex(['2016-01', '2016-04', '2016-07', '2016-10', '2017-01', '2017-04',\n",
" '2017-07', '2017-10'],\n",
" dtype='period[M]')"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"quarters.asfreq(\"M\", how=\"start\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And we can zoom out:"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PeriodIndex(['2016', '2016', '2016', '2016', '2017', '2017', '2017', '2017'], dtype='period[A-DEC]')"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"quarters.asfreq(\"Y\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Of course, we can create a `Series` with a `PeriodIndex`:"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2016Q1 300\n",
"2016Q2 320\n",
"2016Q3 290\n",
"2016Q4 390\n",
"2017Q1 320\n",
"2017Q2 360\n",
"2017Q3 310\n",
"2017Q4 410\n",
"Freq: Q-DEC, dtype: int64"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"quarterly_revenue = pd.Series([300, 320, 290, 390, 320, 360, 310, 410], index=quarters)\n",
"quarterly_revenue"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
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H6qLJI0lNjAnoRd4ydwM/fa2EOWPS+NqCMwK2X38REX584Xia2zr57btOq8M5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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"quarterly_revenue.plot(kind=\"line\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can convert periods to timestamps by calling `to_timestamp`. By default, it will give us the first day of each period, but by setting `how` and `freq`, we can get the last hour of each period:"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2016-03-31 23:59:59.999999999 300\n",
"2016-06-30 23:59:59.999999999 320\n",
"2016-09-30 23:59:59.999999999 290\n",
"2016-12-31 23:59:59.999999999 390\n",
"2017-03-31 23:59:59.999999999 320\n",
"2017-06-30 23:59:59.999999999 360\n",
"2017-09-30 23:59:59.999999999 310\n",
"2017-12-31 23:59:59.999999999 410\n",
"dtype: int64"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"last_hours = quarterly_revenue.to_timestamp(how=\"end\", freq=\"h\")\n",
"last_hours"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And back to periods by calling `to_period`:"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2016Q1 300\n",
"2016Q2 320\n",
"2016Q3 290\n",
"2016Q4 390\n",
"2017Q1 320\n",
"2017Q2 360\n",
"2017Q3 310\n",
"2017Q4 410\n",
"Freq: Q-DEC, dtype: int64"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"last_hours.to_period()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pandas also provides many other time-related functions that we recommend you check out in the [documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html). To whet your appetite, here is one way to get the last business day of each month in 2016, at 9am:"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PeriodIndex(['2016-01-29 09:00', '2016-02-29 09:00', '2016-03-31 09:00',\n",
" '2016-04-29 09:00', '2016-05-31 09:00', '2016-06-30 09:00',\n",
" '2016-07-29 09:00', '2016-08-31 09:00', '2016-09-30 09:00',\n",
" '2016-10-31 09:00', '2016-11-30 09:00', '2016-12-30 09:00'],\n",
" dtype='period[H]')"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"months_2016 = pd.period_range(\"2016\", periods=12, freq=\"M\")\n",
"one_day_after_last_days = months_2016.asfreq(\"D\") + 1\n",
"last_bdays = one_day_after_last_days.to_timestamp() - pd.tseries.offsets.BDay()\n",
"last_bdays.to_period(\"h\") + 9"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# `DataFrame` objects\n",
"A DataFrame object represents a spreadsheet, with cell values, column names and row index labels. You can define expressions to compute columns based on other columns, create pivot-tables, group rows, draw graphs, etc. You can see `DataFrame`s as dictionaries of `Series`.\n",
"\n",
"## Creating a `DataFrame`\n",
"You can create a DataFrame by passing a dictionary of `Series` objects:"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
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" 34 | \n",
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" 0.0 | \n",
" 25.335002 | \n",
" True | \n",
"
\n",
" \n",
" | charles | \n",
" NaN | \n",
" 185 | \n",
" 112 | \n",
" 26 | \n",
" False | \n",
" 5.0 | \n",
" 32.724617 | \n",
" True | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" hobby | \n",
" height | \n",
" weight | \n",
" age | \n",
" over 30 | \n",
" pets | \n",
" body_mass_index | \n",
" overweight | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" Biking | \n",
" 172 | \n",
" 68 | \n",
" 33 | \n",
" True | \n",
" NaN | \n",
" 22.985398 | \n",
" False | \n",
"
\n",
" \n",
" | bob | \n",
" Dancing | \n",
" 181 | \n",
" 83 | \n",
" 34 | \n",
" True | \n",
" 0.0 | \n",
" 25.335002 | \n",
" True | \n",
"
\n",
" \n",
" | charles | \n",
" NaN | \n",
" 185 | \n",
" 112 | \n",
" 26 | \n",
" False | \n",
" 5.0 | \n",
" 32.724617 | \n",
" True | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" hobby | \n",
" height | \n",
" weight | \n",
" age | \n",
" over 30 | \n",
" pets | \n",
" body_mass_index | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" Biking | \n",
" 172 | \n",
" 68 | \n",
" 33 | \n",
" True | \n",
" NaN | \n",
" 22.985398 | \n",
"
\n",
" \n",
" | bob | \n",
" Dancing | \n",
" 181 | \n",
" 83 | \n",
" 34 | \n",
" True | \n",
" 0.0 | \n",
" 25.335002 | \n",
"
\n",
" \n",
" | charles | \n",
" NaN | \n",
" 185 | \n",
" 112 | \n",
" 26 | \n",
" False | \n",
" 5.0 | \n",
" 32.724617 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" hobby | \n",
" height | \n",
" weight | \n",
" age | \n",
" over 30 | \n",
" pets | \n",
" body_mass_index | \n",
" overweight | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" Biking | \n",
" 172 | \n",
" 68 | \n",
" 33 | \n",
" True | \n",
" NaN | \n",
" 22.985398 | \n",
" False | \n",
"
\n",
" \n",
" | bob | \n",
" Dancing | \n",
" 181 | \n",
" 83 | \n",
" 34 | \n",
" True | \n",
" 0.0 | \n",
" 25.335002 | \n",
" False | \n",
"
\n",
" \n",
" | charles | \n",
" NaN | \n",
" 185 | \n",
" 112 | \n",
" 26 | \n",
" False | \n",
" 5.0 | \n",
" 32.724617 | \n",
" True | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" hobby | \n",
" height | \n",
" weight | \n",
" age | \n",
" over 30 | \n",
" pets | \n",
" body_mass_index | \n",
" overweight | \n",
"
\n",
" \n",
" \n",
" \n",
" | bob | \n",
" Dancing | \n",
" 181 | \n",
" 83 | \n",
" 34 | \n",
" True | \n",
" 0.0 | \n",
" 25.335002 | \n",
" False | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" hobby | \n",
" height | \n",
" weight | \n",
" age | \n",
" over 30 | \n",
" pets | \n",
" body_mass_index | \n",
" overweight | \n",
"
\n",
" \n",
" \n",
" \n",
" | charles | \n",
" NaN | \n",
" 185 | \n",
" 112 | \n",
" 26 | \n",
" False | \n",
" 5.0 | \n",
" 32.724617 | \n",
" True | \n",
"
\n",
" \n",
" | bob | \n",
" Dancing | \n",
" 181 | \n",
" 83 | \n",
" 34 | \n",
" True | \n",
" 0.0 | \n",
" 25.335002 | \n",
" False | \n",
"
\n",
" \n",
" | alice | \n",
" Biking | \n",
" 172 | \n",
" 68 | \n",
" 33 | \n",
" True | \n",
" NaN | \n",
" 22.985398 | \n",
" False | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" age | \n",
" body_mass_index | \n",
" height | \n",
" hobby | \n",
" over 30 | \n",
" overweight | \n",
" pets | \n",
" weight | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 33 | \n",
" 22.985398 | \n",
" 172 | \n",
" Biking | \n",
" True | \n",
" False | \n",
" NaN | \n",
" 68 | \n",
"
\n",
" \n",
" | bob | \n",
" 34 | \n",
" 25.335002 | \n",
" 181 | \n",
" Dancing | \n",
" True | \n",
" False | \n",
" 0.0 | \n",
" 83 | \n",
"
\n",
" \n",
" | charles | \n",
" 26 | \n",
" 32.724617 | \n",
" 185 | \n",
" NaN | \n",
" False | \n",
" True | \n",
" 5.0 | \n",
" 112 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" age | \n",
" body_mass_index | \n",
" height | \n",
" hobby | \n",
" over 30 | \n",
" overweight | \n",
" pets | \n",
" weight | \n",
"
\n",
" \n",
" \n",
" \n",
" | charles | \n",
" 26 | \n",
" 32.724617 | \n",
" 185 | \n",
" NaN | \n",
" False | \n",
" True | \n",
" 5.0 | \n",
" 112 | \n",
"
\n",
" \n",
" | alice | \n",
" 33 | \n",
" 22.985398 | \n",
" 172 | \n",
" Biking | \n",
" True | \n",
" False | \n",
" NaN | \n",
" 68 | \n",
"
\n",
" \n",
" | bob | \n",
" 34 | \n",
" 25.335002 | \n",
" 181 | \n",
" Dancing | \n",
" True | \n",
" False | \n",
" 0.0 | \n",
" 83 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 8 | \n",
" 8 | \n",
" 9 | \n",
"
\n",
" \n",
" | bob | \n",
" 10 | \n",
" 9 | \n",
" 9 | \n",
"
\n",
" \n",
" | charles | \n",
" 4 | \n",
" 8 | \n",
" 2 | \n",
"
\n",
" \n",
" | darwin | \n",
" 9 | \n",
" 10 | \n",
" 10 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 2.828427 | \n",
" 2.828427 | \n",
" 3.000000 | \n",
"
\n",
" \n",
" | bob | \n",
" 3.162278 | \n",
" 3.000000 | \n",
" 3.000000 | \n",
"
\n",
" \n",
" | charles | \n",
" 2.000000 | \n",
" 2.828427 | \n",
" 1.414214 | \n",
"
\n",
" \n",
" | darwin | \n",
" 3.000000 | \n",
" 3.162278 | \n",
" 3.162278 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 9 | \n",
" 9 | \n",
" 10 | \n",
"
\n",
" \n",
" | bob | \n",
" 11 | \n",
" 10 | \n",
" 10 | \n",
"
\n",
" \n",
" | charles | \n",
" 5 | \n",
" 9 | \n",
" 3 | \n",
"
\n",
" \n",
" | darwin | \n",
" 10 | \n",
" 11 | \n",
" 11 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" True | \n",
" True | \n",
" True | \n",
"
\n",
" \n",
" | bob | \n",
" True | \n",
" True | \n",
" True | \n",
"
\n",
" \n",
" | charles | \n",
" False | \n",
" True | \n",
" False | \n",
"
\n",
" \n",
" | darwin | \n",
" True | \n",
" True | \n",
" True | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 0.25 | \n",
" -0.75 | \n",
" 1.5 | \n",
"
\n",
" \n",
" | bob | \n",
" 2.25 | \n",
" 0.25 | \n",
" 1.5 | \n",
"
\n",
" \n",
" | charles | \n",
" -3.75 | \n",
" -0.75 | \n",
" -5.5 | \n",
"
\n",
" \n",
" | darwin | \n",
" 1.25 | \n",
" 1.25 | \n",
" 2.5 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 7.75 | \n",
" 8.75 | \n",
" 7.5 | \n",
"
\n",
" \n",
" | bob | \n",
" 7.75 | \n",
" 8.75 | \n",
" 7.5 | \n",
"
\n",
" \n",
" | charles | \n",
" 7.75 | \n",
" 8.75 | \n",
" 7.5 | \n",
"
\n",
" \n",
" | darwin | \n",
" 7.75 | \n",
" 8.75 | \n",
" 7.5 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 0.0 | \n",
" 0.0 | \n",
" 1.0 | \n",
"
\n",
" \n",
" | bob | \n",
" 2.0 | \n",
" 1.0 | \n",
" 1.0 | \n",
"
\n",
" \n",
" | charles | \n",
" -4.0 | \n",
" 0.0 | \n",
" -6.0 | \n",
"
\n",
" \n",
" | darwin | \n",
" 1.0 | \n",
" 2.0 | \n",
" 2.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" oct | \n",
" nov | \n",
" dec | \n",
"
\n",
" \n",
" \n",
" \n",
" | bob | \n",
" 0.0 | \n",
" NaN | \n",
" 2.0 | \n",
"
\n",
" \n",
" | colin | \n",
" NaN | \n",
" 1.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | darwin | \n",
" 0.0 | \n",
" 1.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | charles | \n",
" 3.0 | \n",
" 3.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" dec | \n",
" nov | \n",
" oct | \n",
" sep | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" | bob | \n",
" NaN | \n",
" NaN | \n",
" 9.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | charles | \n",
" NaN | \n",
" 5.0 | \n",
" 11.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | colin | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" | darwin | \n",
" NaN | \n",
" 11.0 | \n",
" 10.0 | \n",
" NaN | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" dec | \n",
" nov | \n",
" oct | \n",
" sep | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | bob | \n",
" 0.0 | \n",
" 0.0 | \n",
" 9.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | charles | \n",
" 0.0 | \n",
" 5.0 | \n",
" 11.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | colin | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | darwin | \n",
" 0.0 | \n",
" 11.0 | \n",
" 10.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" dec | \n",
" nov | \n",
" oct | \n",
" sep | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" NaN | \n",
" 9.0 | \n",
" 8.0 | \n",
" 8.0 | \n",
"
\n",
" \n",
" | bob | \n",
" NaN | \n",
" 9.0 | \n",
" 9.0 | \n",
" 10.0 | \n",
"
\n",
" \n",
" | charles | \n",
" NaN | \n",
" 5.0 | \n",
" 11.0 | \n",
" 4.0 | \n",
"
\n",
" \n",
" | colin | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" | darwin | \n",
" NaN | \n",
" 11.0 | \n",
" 10.0 | \n",
" 9.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" oct | \n",
" nov | \n",
" dec | \n",
"
\n",
" \n",
" \n",
" \n",
" | bob | \n",
" 0.0 | \n",
" NaN | \n",
" 2.0 | \n",
"
\n",
" \n",
" | colin | \n",
" NaN | \n",
" 1.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | darwin | \n",
" 0.0 | \n",
" 1.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | charles | \n",
" 3.0 | \n",
" 3.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" oct | \n",
" nov | \n",
" dec | \n",
"
\n",
" \n",
" \n",
" \n",
" | bob | \n",
" 0.0 | \n",
" 1.0 | \n",
" 2.0 | \n",
"
\n",
" \n",
" | colin | \n",
" NaN | \n",
" 1.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | darwin | \n",
" 0.0 | \n",
" 1.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | charles | \n",
" 3.0 | \n",
" 3.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
" dec | \n",
"
\n",
" \n",
" \n",
" \n",
" | bob | \n",
" 0.0 | \n",
" 0.0 | \n",
" 1.0 | \n",
" 2.0 | \n",
"
\n",
" \n",
" | colin | \n",
" 0.0 | \n",
" 0.5 | \n",
" 1.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | darwin | \n",
" 0.0 | \n",
" 0.0 | \n",
" 1.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | charles | \n",
" 0.0 | \n",
" 3.0 | \n",
" 3.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | alice | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" dec | \n",
" nov | \n",
" oct | \n",
" sep | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" NaN | \n",
" 9.0 | \n",
" 8.0 | \n",
" 8.0 | \n",
"
\n",
" \n",
" | bob | \n",
" NaN | \n",
" 10.0 | \n",
" 9.0 | \n",
" 10.0 | \n",
"
\n",
" \n",
" | charles | \n",
" NaN | \n",
" 5.0 | \n",
" 11.0 | \n",
" 4.0 | \n",
"
\n",
" \n",
" | colin | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" | darwin | \n",
" NaN | \n",
" 11.0 | \n",
" 10.0 | \n",
" 9.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
" dec | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 8.0 | \n",
" 8.0 | \n",
" 9.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | bob | \n",
" 10.0 | \n",
" 9.0 | \n",
" 10.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | charles | \n",
" 4.0 | \n",
" 11.0 | \n",
" 5.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | colin | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" | darwin | \n",
" 9.0 | \n",
" 10.0 | \n",
" 11.0 | \n",
" NaN | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
" dec | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 8.0 | \n",
" 8.0 | \n",
" 9.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | bob | \n",
" 10.0 | \n",
" 9.0 | \n",
" 10.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | charles | \n",
" 4.0 | \n",
" 11.0 | \n",
" 5.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | darwin | \n",
" 9.0 | \n",
" 10.0 | \n",
" 11.0 | \n",
" NaN | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 8.0 | \n",
" 8.0 | \n",
" 9.0 | \n",
"
\n",
" \n",
" | bob | \n",
" 10.0 | \n",
" 9.0 | \n",
" 10.0 | \n",
"
\n",
" \n",
" | charles | \n",
" 4.0 | \n",
" 11.0 | \n",
" 5.0 | \n",
"
\n",
" \n",
" | darwin | \n",
" 9.0 | \n",
" 10.0 | \n",
" 11.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
" dec | \n",
" hobby | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 8.0 | \n",
" 8.0 | \n",
" 9.0 | \n",
" NaN | \n",
" Biking | \n",
"
\n",
" \n",
" | bob | \n",
" 10.0 | \n",
" 9.0 | \n",
" 10.0 | \n",
" NaN | \n",
" Dancing | \n",
"
\n",
" \n",
" | charles | \n",
" 4.0 | \n",
" 11.0 | \n",
" 5.0 | \n",
" NaN | \n",
" NaN | \n",
"
\n",
" \n",
" | colin | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" Dancing | \n",
"
\n",
" \n",
" | darwin | \n",
" 9.0 | \n",
" 10.0 | \n",
" 11.0 | \n",
" NaN | \n",
" Biking | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" sep | \n",
" oct | \n",
" nov | \n",
" dec | \n",
"
\n",
" \n",
" | hobby | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" | Biking | \n",
" 8.5 | \n",
" 9.0 | \n",
" 10.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | Dancing | \n",
" 10.0 | \n",
" 9.0 | \n",
" 10.0 | \n",
" NaN | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" oct | \n",
" nov | \n",
" dec | \n",
"
\n",
" \n",
" \n",
" \n",
" | bob | \n",
" 0.0 | \n",
" NaN | \n",
" 2.0 | \n",
"
\n",
" \n",
" | colin | \n",
" NaN | \n",
" 1.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | darwin | \n",
" 0.0 | \n",
" 1.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | charles | \n",
" 3.0 | \n",
" 3.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" name | \n",
" month | \n",
" grade | \n",
" bonus | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" alice | \n",
" sep | \n",
" 8.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | 1 | \n",
" alice | \n",
" oct | \n",
" 8.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | 2 | \n",
" alice | \n",
" nov | \n",
" 9.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | 3 | \n",
" bob | \n",
" sep | \n",
" 10.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 4 | \n",
" bob | \n",
" oct | \n",
" 9.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | 5 | \n",
" bob | \n",
" nov | \n",
" 10.0 | \n",
" 2.0 | \n",
"
\n",
" \n",
" | 6 | \n",
" charles | \n",
" sep | \n",
" 4.0 | \n",
" 3.0 | \n",
"
\n",
" \n",
" | 7 | \n",
" charles | \n",
" oct | \n",
" 11.0 | \n",
" 3.0 | \n",
"
\n",
" \n",
" | 8 | \n",
" charles | \n",
" nov | \n",
" 5.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 9 | \n",
" darwin | \n",
" sep | \n",
" 9.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 10 | \n",
" darwin | \n",
" oct | \n",
" 10.0 | \n",
" 1.0 | \n",
"
\n",
" \n",
" | 11 | \n",
" darwin | \n",
" nov | \n",
" 11.0 | \n",
" 0.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" bonus | \n",
" grade | \n",
"
\n",
" \n",
" | name | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" NaN | \n",
" 8.333333 | \n",
"
\n",
" \n",
" | bob | \n",
" 1.000000 | \n",
" 9.666667 | \n",
"
\n",
" \n",
" | charles | \n",
" 2.000000 | \n",
" 6.666667 | \n",
"
\n",
" \n",
" | darwin | \n",
" 0.333333 | \n",
" 10.000000 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" bonus | \n",
" grade | \n",
"
\n",
" \n",
" | name | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" NaN | \n",
" 9.0 | \n",
"
\n",
" \n",
" | bob | \n",
" 2.0 | \n",
" 10.0 | \n",
"
\n",
" \n",
" | charles | \n",
" 3.0 | \n",
" 11.0 | \n",
"
\n",
" \n",
" | darwin | \n",
" 1.0 | \n",
" 11.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | month | \n",
" nov | \n",
" oct | \n",
" sep | \n",
" All | \n",
"
\n",
" \n",
" | name | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" 9.00 | \n",
" 8.0 | \n",
" 8.00 | \n",
" 8.333333 | \n",
"
\n",
" \n",
" | bob | \n",
" 10.00 | \n",
" 9.0 | \n",
" 10.00 | \n",
" 9.666667 | \n",
"
\n",
" \n",
" | charles | \n",
" 5.00 | \n",
" 11.0 | \n",
" 4.00 | \n",
" 6.666667 | \n",
"
\n",
" \n",
" | darwin | \n",
" 11.00 | \n",
" 10.0 | \n",
" 9.00 | \n",
" 10.000000 | \n",
"
\n",
" \n",
" | All | \n",
" 8.75 | \n",
" 9.5 | \n",
" 7.75 | \n",
" 8.666667 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" | \n",
" bonus | \n",
" grade | \n",
"
\n",
" \n",
" | name | \n",
" month | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" nov | \n",
" NaN | \n",
" 9.00 | \n",
"
\n",
" \n",
" | oct | \n",
" NaN | \n",
" 8.00 | \n",
"
\n",
" \n",
" | sep | \n",
" NaN | \n",
" 8.00 | \n",
"
\n",
" \n",
" | bob | \n",
" nov | \n",
" 2.000 | \n",
" 10.00 | \n",
"
\n",
" \n",
" | oct | \n",
" NaN | \n",
" 9.00 | \n",
"
\n",
" \n",
" | sep | \n",
" 0.000 | \n",
" 10.00 | \n",
"
\n",
" \n",
" | charles | \n",
" nov | \n",
" 0.000 | \n",
" 5.00 | \n",
"
\n",
" \n",
" | oct | \n",
" 3.000 | \n",
" 11.00 | \n",
"
\n",
" \n",
" | sep | \n",
" 3.000 | \n",
" 4.00 | \n",
"
\n",
" \n",
" | darwin | \n",
" nov | \n",
" 0.000 | \n",
" 11.00 | \n",
"
\n",
" \n",
" | oct | \n",
" 1.000 | \n",
" 10.00 | \n",
"
\n",
" \n",
" | sep | \n",
" 0.000 | \n",
" 9.00 | \n",
"
\n",
" \n",
" | All | \n",
" | \n",
" 1.125 | \n",
" 8.75 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
" C | \n",
" some_text | \n",
" D | \n",
" E | \n",
" F | \n",
" G | \n",
" H | \n",
" I | \n",
" ... | \n",
" Q | \n",
" R | \n",
" S | \n",
" T | \n",
" U | \n",
" V | \n",
" W | \n",
" X | \n",
" Y | \n",
" Z | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" NaN | \n",
" 11.0 | \n",
" 44.0 | \n",
" Blabla | \n",
" 99.0 | \n",
" NaN | \n",
" 88.0 | \n",
" 22.0 | \n",
" 165.0 | \n",
" 143.0 | \n",
" ... | \n",
" 11.0 | \n",
" NaN | \n",
" 11.0 | \n",
" 44.0 | \n",
" 99.0 | \n",
" NaN | \n",
" 88.0 | \n",
" 22.0 | \n",
" 165.0 | \n",
" 143.0 | \n",
"
\n",
" \n",
" | 1 | \n",
" 11.0 | \n",
" 22.0 | \n",
" 55.0 | \n",
" Blabla | \n",
" 110.0 | \n",
" NaN | \n",
" 99.0 | \n",
" 33.0 | \n",
" NaN | \n",
" 154.0 | \n",
" ... | \n",
" 22.0 | \n",
" 11.0 | \n",
" 22.0 | \n",
" 55.0 | \n",
" 110.0 | \n",
" NaN | \n",
" 99.0 | \n",
" 33.0 | \n",
" NaN | \n",
" 154.0 | \n",
"
\n",
" \n",
" | 2 | \n",
" 22.0 | \n",
" 33.0 | \n",
" 66.0 | \n",
" Blabla | \n",
" 121.0 | \n",
" 11.0 | \n",
" 110.0 | \n",
" 44.0 | \n",
" NaN | \n",
" 165.0 | \n",
" ... | \n",
" 33.0 | \n",
" 22.0 | \n",
" 33.0 | \n",
" 66.0 | \n",
" 121.0 | \n",
" 11.0 | \n",
" 110.0 | \n",
" 44.0 | \n",
" NaN | \n",
" 165.0 | \n",
"
\n",
" \n",
" | 3 | \n",
" 33.0 | \n",
" 44.0 | \n",
" 77.0 | \n",
" Blabla | \n",
" 132.0 | \n",
" 22.0 | \n",
" 121.0 | \n",
" 55.0 | \n",
" 11.0 | \n",
" NaN | \n",
" ... | \n",
" 44.0 | \n",
" 33.0 | \n",
" 44.0 | \n",
" 77.0 | \n",
" 132.0 | \n",
" 22.0 | \n",
" 121.0 | \n",
" 55.0 | \n",
" 11.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | 4 | \n",
" 44.0 | \n",
" 55.0 | \n",
" 88.0 | \n",
" Blabla | \n",
" 143.0 | \n",
" 33.0 | \n",
" 132.0 | \n",
" 66.0 | \n",
" 22.0 | \n",
" NaN | \n",
" ... | \n",
" 55.0 | \n",
" 44.0 | \n",
" 55.0 | \n",
" 88.0 | \n",
" 143.0 | \n",
" 33.0 | \n",
" 132.0 | \n",
" 66.0 | \n",
" 22.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
"
\n",
" \n",
" | 9995 | \n",
" NaN | \n",
" NaN | \n",
" 33.0 | \n",
" Blabla | \n",
" 88.0 | \n",
" 165.0 | \n",
" 77.0 | \n",
" 11.0 | \n",
" 154.0 | \n",
" 132.0 | \n",
" ... | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 33.0 | \n",
" 88.0 | \n",
" 165.0 | \n",
" 77.0 | \n",
" 11.0 | \n",
" 154.0 | \n",
" 132.0 | \n",
"
\n",
" \n",
" | 9996 | \n",
" NaN | \n",
" 11.0 | \n",
" 44.0 | \n",
" Blabla | \n",
" 99.0 | \n",
" NaN | \n",
" 88.0 | \n",
" 22.0 | \n",
" 165.0 | \n",
" 143.0 | \n",
" ... | \n",
" 11.0 | \n",
" NaN | \n",
" 11.0 | \n",
" 44.0 | \n",
" 99.0 | \n",
" NaN | \n",
" 88.0 | \n",
" 22.0 | \n",
" 165.0 | \n",
" 143.0 | \n",
"
\n",
" \n",
" | 9997 | \n",
" 11.0 | \n",
" 22.0 | \n",
" 55.0 | \n",
" Blabla | \n",
" 110.0 | \n",
" NaN | \n",
" 99.0 | \n",
" 33.0 | \n",
" NaN | \n",
" 154.0 | \n",
" ... | \n",
" 22.0 | \n",
" 11.0 | \n",
" 22.0 | \n",
" 55.0 | \n",
" 110.0 | \n",
" NaN | \n",
" 99.0 | \n",
" 33.0 | \n",
" NaN | \n",
" 154.0 | \n",
"
\n",
" \n",
" | 9998 | \n",
" 22.0 | \n",
" 33.0 | \n",
" 66.0 | \n",
" Blabla | \n",
" 121.0 | \n",
" 11.0 | \n",
" 110.0 | \n",
" 44.0 | \n",
" NaN | \n",
" 165.0 | \n",
" ... | \n",
" 33.0 | \n",
" 22.0 | \n",
" 33.0 | \n",
" 66.0 | \n",
" 121.0 | \n",
" 11.0 | \n",
" 110.0 | \n",
" 44.0 | \n",
" NaN | \n",
" 165.0 | \n",
"
\n",
" \n",
" | 9999 | \n",
" 33.0 | \n",
" 44.0 | \n",
" 77.0 | \n",
" Blabla | \n",
" 132.0 | \n",
" 22.0 | \n",
" 121.0 | \n",
" 55.0 | \n",
" 11.0 | \n",
" NaN | \n",
" ... | \n",
" 44.0 | \n",
" 33.0 | \n",
" 44.0 | \n",
" 77.0 | \n",
" 132.0 | \n",
" 22.0 | \n",
" 121.0 | \n",
" 55.0 | \n",
" 11.0 | \n",
" NaN | \n",
"
\n",
" \n",
"
\n",
"
10000 rows × 27 columns
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
" C | \n",
" some_text | \n",
" D | \n",
" E | \n",
" F | \n",
" G | \n",
" H | \n",
" I | \n",
" ... | \n",
" Q | \n",
" R | \n",
" S | \n",
" T | \n",
" U | \n",
" V | \n",
" W | \n",
" X | \n",
" Y | \n",
" Z | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" NaN | \n",
" 11.0 | \n",
" 44.0 | \n",
" Blabla | \n",
" 99.0 | \n",
" NaN | \n",
" 88.0 | \n",
" 22.0 | \n",
" 165.0 | \n",
" 143.0 | \n",
" ... | \n",
" 11.0 | \n",
" NaN | \n",
" 11.0 | \n",
" 44.0 | \n",
" 99.0 | \n",
" NaN | \n",
" 88.0 | \n",
" 22.0 | \n",
" 165.0 | \n",
" 143.0 | \n",
"
\n",
" \n",
" | 1 | \n",
" 11.0 | \n",
" 22.0 | \n",
" 55.0 | \n",
" Blabla | \n",
" 110.0 | \n",
" NaN | \n",
" 99.0 | \n",
" 33.0 | \n",
" NaN | \n",
" 154.0 | \n",
" ... | \n",
" 22.0 | \n",
" 11.0 | \n",
" 22.0 | \n",
" 55.0 | \n",
" 110.0 | \n",
" NaN | \n",
" 99.0 | \n",
" 33.0 | \n",
" NaN | \n",
" 154.0 | \n",
"
\n",
" \n",
" | 2 | \n",
" 22.0 | \n",
" 33.0 | \n",
" 66.0 | \n",
" Blabla | \n",
" 121.0 | \n",
" 11.0 | \n",
" 110.0 | \n",
" 44.0 | \n",
" NaN | \n",
" 165.0 | \n",
" ... | \n",
" 33.0 | \n",
" 22.0 | \n",
" 33.0 | \n",
" 66.0 | \n",
" 121.0 | \n",
" 11.0 | \n",
" 110.0 | \n",
" 44.0 | \n",
" NaN | \n",
" 165.0 | \n",
"
\n",
" \n",
" | 3 | \n",
" 33.0 | \n",
" 44.0 | \n",
" 77.0 | \n",
" Blabla | \n",
" 132.0 | \n",
" 22.0 | \n",
" 121.0 | \n",
" 55.0 | \n",
" 11.0 | \n",
" NaN | \n",
" ... | \n",
" 44.0 | \n",
" 33.0 | \n",
" 44.0 | \n",
" 77.0 | \n",
" 132.0 | \n",
" 22.0 | \n",
" 121.0 | \n",
" 55.0 | \n",
" 11.0 | \n",
" NaN | \n",
"
\n",
" \n",
" | 4 | \n",
" 44.0 | \n",
" 55.0 | \n",
" 88.0 | \n",
" Blabla | \n",
" 143.0 | \n",
" 33.0 | \n",
" 132.0 | \n",
" 66.0 | \n",
" 22.0 | \n",
" NaN | \n",
" ... | \n",
" 55.0 | \n",
" 44.0 | \n",
" 55.0 | \n",
" 88.0 | \n",
" 143.0 | \n",
" 33.0 | \n",
" 132.0 | \n",
" 66.0 | \n",
" 22.0 | \n",
" NaN | \n",
"
\n",
" \n",
"
\n",
"
5 rows × 27 columns
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
" C | \n",
" some_text | \n",
" D | \n",
" E | \n",
" F | \n",
" G | \n",
" H | \n",
" I | \n",
" ... | \n",
" Q | \n",
" R | \n",
" S | \n",
" T | \n",
" U | \n",
" V | \n",
" W | \n",
" X | \n",
" Y | \n",
" Z | \n",
"
\n",
" \n",
" \n",
" \n",
" | 9998 | \n",
" 22.0 | \n",
" 33.0 | \n",
" 66.0 | \n",
" Blabla | \n",
" 121.0 | \n",
" 11.0 | \n",
" 110.0 | \n",
" 44.0 | \n",
" NaN | \n",
" 165.0 | \n",
" ... | \n",
" 33.0 | \n",
" 22.0 | \n",
" 33.0 | \n",
" 66.0 | \n",
" 121.0 | \n",
" 11.0 | \n",
" 110.0 | \n",
" 44.0 | \n",
" NaN | \n",
" 165.0 | \n",
"
\n",
" \n",
" | 9999 | \n",
" 33.0 | \n",
" 44.0 | \n",
" 77.0 | \n",
" Blabla | \n",
" 132.0 | \n",
" 22.0 | \n",
" 121.0 | \n",
" 55.0 | \n",
" 11.0 | \n",
" NaN | \n",
" ... | \n",
" 44.0 | \n",
" 33.0 | \n",
" 44.0 | \n",
" 77.0 | \n",
" 132.0 | \n",
" 22.0 | \n",
" 121.0 | \n",
" 55.0 | \n",
" 11.0 | \n",
" NaN | \n",
"
\n",
" \n",
"
\n",
"
2 rows × 27 columns
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
" C | \n",
" D | \n",
" E | \n",
" F | \n",
" G | \n",
" H | \n",
" I | \n",
" J | \n",
" ... | \n",
" Q | \n",
" R | \n",
" S | \n",
" T | \n",
" U | \n",
" V | \n",
" W | \n",
" X | \n",
" Y | \n",
" Z | \n",
"
\n",
" \n",
" \n",
" \n",
" | count | \n",
" 8823.000000 | \n",
" 8824.000000 | \n",
" 8824.000000 | \n",
" 8824.000000 | \n",
" 8822.000000 | \n",
" 8824.000000 | \n",
" 8824.000000 | \n",
" 8822.000000 | \n",
" 8823.000000 | \n",
" 8823.000000 | \n",
" ... | \n",
" 8824.000000 | \n",
" 8823.000000 | \n",
" 8824.000000 | \n",
" 8824.000000 | \n",
" 8824.000000 | \n",
" 8822.000000 | \n",
" 8824.000000 | \n",
" 8824.000000 | \n",
" 8822.000000 | \n",
" 8823.000000 | \n",
"
\n",
" \n",
" | mean | \n",
" 87.977559 | \n",
" 87.972575 | \n",
" 87.987534 | \n",
" 88.012466 | \n",
" 87.983791 | \n",
" 88.007480 | \n",
" 87.977561 | \n",
" 88.000000 | \n",
" 88.022441 | \n",
" 88.022441 | \n",
" ... | \n",
" 87.972575 | \n",
" 87.977559 | \n",
" 87.972575 | \n",
" 87.987534 | \n",
" 88.012466 | \n",
" 87.983791 | \n",
" 88.007480 | \n",
" 87.977561 | \n",
" 88.000000 | \n",
" 88.022441 | \n",
"
\n",
" \n",
" | std | \n",
" 47.535911 | \n",
" 47.535523 | \n",
" 47.521679 | \n",
" 47.521679 | \n",
" 47.535001 | \n",
" 47.519371 | \n",
" 47.529755 | \n",
" 47.536879 | \n",
" 47.535911 | \n",
" 47.535911 | \n",
" ... | \n",
" 47.535523 | \n",
" 47.535911 | \n",
" 47.535523 | \n",
" 47.521679 | \n",
" 47.521679 | \n",
" 47.535001 | \n",
" 47.519371 | \n",
" 47.529755 | \n",
" 47.536879 | \n",
" 47.535911 | \n",
"
\n",
" \n",
" | min | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" ... | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
" 11.000000 | \n",
"
\n",
" \n",
" | 25% | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" ... | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
" 44.000000 | \n",
"
\n",
" \n",
" | 50% | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" ... | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
" 88.000000 | \n",
"
\n",
" \n",
" | 75% | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" ... | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
" 132.000000 | \n",
"
\n",
" \n",
" | max | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" ... | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
" 165.000000 | \n",
"
\n",
" \n",
"
\n",
"
8 rows × 26 columns
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" hobby | \n",
" weight | \n",
" birthyear | \n",
" children | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" Biking | \n",
" 68.5 | \n",
" 1985 | \n",
" NaN | \n",
"
\n",
" \n",
" | bob | \n",
" Dancing | \n",
" 83.1 | \n",
" 1984 | \n",
" 3.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
" \n",
" \n",
" | \n",
" hobby | \n",
" weight | \n",
" birthyear | \n",
" children | \n",
"
\n",
" \n",
" \n",
" \n",
" | alice | \n",
" Biking | \n",
" 68.5 | \n",
" 1985 | \n",
" NaN | \n",
"
\n",
" \n",
" | bob | \n",
" Dancing | \n",
" 83.1 | \n",
" 1984 | \n",
" 3.0 | \n",
"
\n",
" \n",
"
\n",
"\n",
"# my_df.json\n",
"{\"hobby\":{\"alice\":\"Biking\",\"bob\":\"Dancing\"},\"weight\":{\"alice\":68.5,\"bob\":83.1},\"birthyear\":{\"alice\":1985,\"bob\":1984},\"children\":{\"alice\":null,\"bob\":3.0}}\n",
"\n"
]
}
],
"source": [
"from pathlib import Path\n",
"\n",
"for filename in (\"my_df.csv\", \"my_df.html\", \"my_df.json\"):\n",
" print(\"#\", filename)\n",
" print(Path(filename).read_text())\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the index is saved as the first column in a CSV file, as `` tags in HTML and as keys in JSON. The index column has no name by default, but you can fix that by setting `my_df.index.name` to any name you want.\n",
"\n",
"Saving to other formats works very similarly, but some formats require extra libraries to be installed. For example, saving to Excel requires the openpyxl library:"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {},
"outputs": [],
"source": [
"try:\n",
" my_df.to_excel(\"my_df.xlsx\", sheet_name='People')\n",
"except ImportError as e:\n",
" print(e)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading\n",
"Now let's load our CSV file back into a `DataFrame`:"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" hobby | \n",
" weight | \n",
" birthyear | \n",
" children | \n",
" \n",
" \n",
" \n",
" \n",
" | alice | \n",
" Biking | \n",
" 68.5 | \n",
" 1985 | \n",
" NaN | \n",
" \n",
" \n",
" | bob | \n",
" Dancing | \n",
" 83.1 | \n",
" 1984 | \n",
" 3.0 | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" State | \n",
" Population | \n",
" lat | \n",
" lon | \n",
" \n",
" \n",
" | City | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" \n",
" \n",
" \n",
" \n",
" | Marysville | \n",
" Washington | \n",
" 63269 | \n",
" 48.051764 | \n",
" -122.177082 | \n",
" \n",
" \n",
" | Perris | \n",
" California | \n",
" 72326 | \n",
" 33.782519 | \n",
" -117.228648 | \n",
" \n",
" \n",
" | Cleveland | \n",
" Ohio | \n",
" 390113 | \n",
" 41.499320 | \n",
" -81.694361 | \n",
" \n",
" \n",
" | Worcester | \n",
" Massachusetts | \n",
" 182544 | \n",
" 42.262593 | \n",
" -71.802293 | \n",
" \n",
" \n",
" | Columbia | \n",
" South Carolina | \n",
" 133358 | \n",
" 34.000710 | \n",
" -81.034814 | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" state | \n",
" city | \n",
" lat | \n",
" lng | \n",
" \n",
" \n",
" \n",
" \n",
" | 0 | \n",
" CA | \n",
" San Francisco | \n",
" 37.781334 | \n",
" -122.416728 | \n",
" \n",
" \n",
" | 1 | \n",
" NY | \n",
" New York | \n",
" 40.705649 | \n",
" -74.008344 | \n",
" \n",
" \n",
" | 2 | \n",
" FL | \n",
" Miami | \n",
" 25.791100 | \n",
" -80.320733 | \n",
" \n",
" \n",
" | 3 | \n",
" OH | \n",
" Cleveland | \n",
" 41.473508 | \n",
" -81.739791 | \n",
" \n",
" \n",
" | 4 | \n",
" UT | \n",
" Salt Lake City | \n",
" 40.755851 | \n",
" -111.896657 | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" population | \n",
" city | \n",
" state | \n",
" \n",
" \n",
" \n",
" \n",
" | 3 | \n",
" 808976 | \n",
" San Francisco | \n",
" California | \n",
" \n",
" \n",
" | 4 | \n",
" 8363710 | \n",
" New York | \n",
" New-York | \n",
" \n",
" \n",
" | 5 | \n",
" 413201 | \n",
" Miami | \n",
" Florida | \n",
" \n",
" \n",
" | 6 | \n",
" 2242193 | \n",
" Houston | \n",
" Texas | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" state_x | \n",
" city | \n",
" lat | \n",
" lng | \n",
" population | \n",
" state_y | \n",
" \n",
" \n",
" \n",
" \n",
" | 0 | \n",
" CA | \n",
" San Francisco | \n",
" 37.781334 | \n",
" -122.416728 | \n",
" 808976 | \n",
" California | \n",
" \n",
" \n",
" | 1 | \n",
" NY | \n",
" New York | \n",
" 40.705649 | \n",
" -74.008344 | \n",
" 8363710 | \n",
" New-York | \n",
" \n",
" \n",
" | 2 | \n",
" FL | \n",
" Miami | \n",
" 25.791100 | \n",
" -80.320733 | \n",
" 413201 | \n",
" Florida | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" state_x | \n",
" city | \n",
" lat | \n",
" lng | \n",
" population | \n",
" state_y | \n",
" \n",
" \n",
" \n",
" \n",
" | 0 | \n",
" CA | \n",
" San Francisco | \n",
" 37.781334 | \n",
" -122.416728 | \n",
" 808976.0 | \n",
" California | \n",
" \n",
" \n",
" | 1 | \n",
" NY | \n",
" New York | \n",
" 40.705649 | \n",
" -74.008344 | \n",
" 8363710.0 | \n",
" New-York | \n",
" \n",
" \n",
" | 2 | \n",
" FL | \n",
" Miami | \n",
" 25.791100 | \n",
" -80.320733 | \n",
" 413201.0 | \n",
" Florida | \n",
" \n",
" \n",
" | 3 | \n",
" OH | \n",
" Cleveland | \n",
" 41.473508 | \n",
" -81.739791 | \n",
" NaN | \n",
" NaN | \n",
" \n",
" \n",
" | 4 | \n",
" UT | \n",
" Salt Lake City | \n",
" 40.755851 | \n",
" -111.896657 | \n",
" NaN | \n",
" NaN | \n",
" \n",
" \n",
" | 5 | \n",
" NaN | \n",
" Houston | \n",
" NaN | \n",
" NaN | \n",
" 2242193.0 | \n",
" Texas | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" state_x | \n",
" city | \n",
" lat | \n",
" lng | \n",
" population | \n",
" state_y | \n",
" \n",
" \n",
" \n",
" \n",
" | 0 | \n",
" CA | \n",
" San Francisco | \n",
" 37.781334 | \n",
" -122.416728 | \n",
" 808976 | \n",
" California | \n",
" \n",
" \n",
" | 1 | \n",
" NY | \n",
" New York | \n",
" 40.705649 | \n",
" -74.008344 | \n",
" 8363710 | \n",
" New-York | \n",
" \n",
" \n",
" | 2 | \n",
" FL | \n",
" Miami | \n",
" 25.791100 | \n",
" -80.320733 | \n",
" 413201 | \n",
" Florida | \n",
" \n",
" \n",
" | 3 | \n",
" NaN | \n",
" Houston | \n",
" NaN | \n",
" NaN | \n",
" 2242193 | \n",
" Texas | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" state_x | \n",
" city | \n",
" lat | \n",
" lng | \n",
" population | \n",
" name | \n",
" state_y | \n",
" \n",
" \n",
" \n",
" \n",
" | 0 | \n",
" CA | \n",
" San Francisco | \n",
" 37.781334 | \n",
" -122.416728 | \n",
" 808976 | \n",
" San Francisco | \n",
" California | \n",
" \n",
" \n",
" | 1 | \n",
" NY | \n",
" New York | \n",
" 40.705649 | \n",
" -74.008344 | \n",
" 8363710 | \n",
" New York | \n",
" New-York | \n",
" \n",
" \n",
" | 2 | \n",
" FL | \n",
" Miami | \n",
" 25.791100 | \n",
" -80.320733 | \n",
" 413201 | \n",
" Miami | \n",
" Florida | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" state | \n",
" city | \n",
" lat | \n",
" lng | \n",
" population | \n",
" \n",
" \n",
" \n",
" \n",
" | 0 | \n",
" CA | \n",
" San Francisco | \n",
" 37.781334 | \n",
" -122.416728 | \n",
" NaN | \n",
" \n",
" \n",
" | 1 | \n",
" NY | \n",
" New York | \n",
" 40.705649 | \n",
" -74.008344 | \n",
" NaN | \n",
" \n",
" \n",
" | 2 | \n",
" FL | \n",
" Miami | \n",
" 25.791100 | \n",
" -80.320733 | \n",
" NaN | \n",
" \n",
" \n",
" | 3 | \n",
" OH | \n",
" Cleveland | \n",
" 41.473508 | \n",
" -81.739791 | \n",
" NaN | \n",
" \n",
" \n",
" | 4 | \n",
" UT | \n",
" Salt Lake City | \n",
" 40.755851 | \n",
" -111.896657 | \n",
" NaN | \n",
" \n",
" \n",
" | 3 | \n",
" California | \n",
" San Francisco | \n",
" NaN | \n",
" NaN | \n",
" 808976.0 | \n",
" \n",
" \n",
" | 4 | \n",
" New-York | \n",
" New York | \n",
" NaN | \n",
" NaN | \n",
" 8363710.0 | \n",
" \n",
" \n",
" | 5 | \n",
" Florida | \n",
" Miami | \n",
" NaN | \n",
" NaN | \n",
" 413201.0 | \n",
" \n",
" \n",
" | 6 | \n",
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" Houston | \n",
" NaN | \n",
" NaN | \n",
" 2242193.0 | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
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" lng | \n",
" population | \n",
" \n",
" \n",
" \n",
" \n",
" | 3 | \n",
" OH | \n",
" Cleveland | \n",
" 41.473508 | \n",
" -81.739791 | \n",
" NaN | \n",
" \n",
" \n",
" | 3 | \n",
" California | \n",
" San Francisco | \n",
" NaN | \n",
" NaN | \n",
" 808976.0 | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
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" \n",
" | \n",
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" lat | \n",
" lng | \n",
" population | \n",
" \n",
" \n",
" \n",
" \n",
" | 0 | \n",
" CA | \n",
" San Francisco | \n",
" 37.781334 | \n",
" -122.416728 | \n",
" NaN | \n",
" \n",
" \n",
" | 1 | \n",
" NY | \n",
" New York | \n",
" 40.705649 | \n",
" -74.008344 | \n",
" NaN | \n",
" \n",
" \n",
" | 2 | \n",
" FL | \n",
" Miami | \n",
" 25.791100 | \n",
" -80.320733 | \n",
" NaN | \n",
" \n",
" \n",
" | 3 | \n",
" OH | \n",
" Cleveland | \n",
" 41.473508 | \n",
" -81.739791 | \n",
" NaN | \n",
" \n",
" \n",
" | 4 | \n",
" UT | \n",
" Salt Lake City | \n",
" 40.755851 | \n",
" -111.896657 | \n",
" NaN | \n",
" \n",
" \n",
" | 5 | \n",
" California | \n",
" San Francisco | \n",
" NaN | \n",
" NaN | \n",
" 808976.0 | \n",
" \n",
" \n",
" | 6 | \n",
" New-York | \n",
" New York | \n",
" NaN | \n",
" NaN | \n",
" 8363710.0 | \n",
" \n",
" \n",
" | 7 | \n",
" Florida | \n",
" Miami | \n",
" NaN | \n",
" NaN | \n",
" 413201.0 | \n",
" \n",
" \n",
" | 8 | \n",
" Texas | \n",
" Houston | \n",
" NaN | \n",
" NaN | \n",
" 2242193.0 | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
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" \n",
" \n",
" | \n",
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" city | \n",
" \n",
" \n",
" \n",
" \n",
" | 0 | \n",
" CA | \n",
" San Francisco | \n",
" \n",
" \n",
" | 1 | \n",
" NY | \n",
" New York | \n",
" \n",
" \n",
" | 2 | \n",
" FL | \n",
" Miami | \n",
" \n",
" \n",
" | 3 | \n",
" OH | \n",
" Cleveland | \n",
" \n",
" \n",
" | 4 | \n",
" UT | \n",
" Salt Lake City | \n",
" \n",
" \n",
" | 3 | \n",
" California | \n",
" San Francisco | \n",
" \n",
" \n",
" | 4 | \n",
" New-York | \n",
" New York | \n",
" \n",
" \n",
" | 5 | \n",
" Florida | \n",
" Miami | \n",
" \n",
" \n",
" | 6 | \n",
" Texas | \n",
" Houston | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" state | \n",
" city | \n",
" lat | \n",
" lng | \n",
" population | \n",
" city | \n",
" state | \n",
" \n",
" \n",
" \n",
" \n",
" | 0 | \n",
" CA | \n",
" San Francisco | \n",
" 37.781334 | \n",
" -122.416728 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" \n",
" \n",
" | 1 | \n",
" NY | \n",
" New York | \n",
" 40.705649 | \n",
" -74.008344 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" \n",
" \n",
" | 2 | \n",
" FL | \n",
" Miami | \n",
" 25.791100 | \n",
" -80.320733 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" \n",
" \n",
" | 3 | \n",
" OH | \n",
" Cleveland | \n",
" 41.473508 | \n",
" -81.739791 | \n",
" 808976.0 | \n",
" San Francisco | \n",
" California | \n",
" \n",
" \n",
" | 4 | \n",
" UT | \n",
" Salt Lake City | \n",
" 40.755851 | \n",
" -111.896657 | \n",
" 8363710.0 | \n",
" New York | \n",
" New-York | \n",
" \n",
" \n",
" | 5 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 413201.0 | \n",
" Miami | \n",
" Florida | \n",
" \n",
" \n",
" | 6 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 2242193.0 | \n",
" Houston | \n",
" Texas | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" state | \n",
" lat | \n",
" lng | \n",
" population | \n",
" state | \n",
" \n",
" \n",
" | city | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" | \n",
" \n",
" \n",
" \n",
" \n",
" | San Francisco | \n",
" CA | \n",
" 37.781334 | \n",
" -122.416728 | \n",
" 808976.0 | \n",
" California | \n",
" \n",
" \n",
" | New York | \n",
" NY | \n",
" 40.705649 | \n",
" -74.008344 | \n",
" 8363710.0 | \n",
" New-York | \n",
" \n",
" \n",
" | Miami | \n",
" FL | \n",
" 25.791100 | \n",
" -80.320733 | \n",
" 413201.0 | \n",
" Florida | \n",
" \n",
" \n",
" | Cleveland | \n",
" OH | \n",
" 41.473508 | \n",
" -81.739791 | \n",
" NaN | \n",
" NaN | \n",
" \n",
" \n",
" | Salt Lake City | \n",
" UT | \n",
" 40.755851 | \n",
" -111.896657 | \n",
" NaN | \n",
" NaN | \n",
" \n",
" \n",
" | Houston | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 2242193.0 | \n",
" Texas | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" population | \n",
" city | \n",
" state | \n",
" eco_code | \n",
" \n",
" \n",
" \n",
" \n",
" | 3 | \n",
" 808976 | \n",
" San Francisco | \n",
" California | \n",
" 17 | \n",
" \n",
" \n",
" | 4 | \n",
" 8363710 | \n",
" New York | \n",
" New-York | \n",
" 17 | \n",
" \n",
" \n",
" | 5 | \n",
" 413201 | \n",
" Miami | \n",
" Florida | \n",
" 34 | \n",
" \n",
" \n",
" | 6 | \n",
" 2242193 | \n",
" Houston | \n",
" Texas | \n",
" 20 | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" population | \n",
" city | \n",
" state | \n",
" eco_code | \n",
" economy | \n",
" \n",
" \n",
" \n",
" \n",
" | 3 | \n",
" 808976 | \n",
" San Francisco | \n",
" California | \n",
" 17 | \n",
" Banking | \n",
" \n",
" \n",
" | 4 | \n",
" 8363710 | \n",
" New York | \n",
" New-York | \n",
" 17 | \n",
" Banking | \n",
" \n",
" \n",
" | 5 | \n",
" 413201 | \n",
" Miami | \n",
" Florida | \n",
" 34 | \n",
" Tourism | \n",
" \n",
" \n",
" | 6 | \n",
" 2242193 | \n",
" Houston | \n",
" Texas | \n",
" 20 | \n",
" Energy | \n",
" \n",
" \n",
" \n",
" \n",
"\n",
" \n",
" \n",
" \n",
" | \n",
" population | \n",
" city | \n",
" state | \n",
" eco_code | \n",
" economy | \n",
" \n",
" \n",
" \n",
" \n",
" | 3 | \n",
" 808976 | \n",
" San Francisco | \n",
" California | \n",
" 17 | \n",
" Finance | \n",
" \n",
" \n",
" | 4 | \n",
" 8363710 | \n",
" New York | \n",
" New-York | \n",
" 17 | \n",
" Finance | \n",
" \n",
" \n",
" | 5 | \n",
" 413201 | \n",
" Miami | \n",
" Florida | \n",
" 34 | \n",
" Tourism | \n",
" \n",
" \n",
" | 6 | \n",
" 2242193 | \n",
" Houston | \n",
" Texas | \n",
" 20 | \n",
" Energy | \n",
" \n",
" \n",
" \n",
" |