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numpy nan quickguide
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rasbt
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- A collection of useful regular expressions [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/useful_regex.ipynb)]
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- Quick guide for dealing with missing numbers in NumPy [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/numpy_nan_quickguide.ipynb)]
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<br>
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770
tutorials/numpy_nan_quickguide.ipynb
Normal file
770
tutorials/numpy_nan_quickguide.ipynb
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@ -0,0 +1,770 @@
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"[[back to python_reference](https://github.com/rasbt/python_reference)]"
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"%watermark -v -p numpy -d -u"
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"Last updated: 30/07/2014 \n",
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"\n",
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"CPython 3.4.1\n",
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"IPython 2.0.0\n",
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"<font size=\"1.5em\">[More information](https://github.com/rasbt/watermark) about the `watermark` magic command extension.</font>"
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"cell_type": "heading",
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"Quick guide for dealing with missing numbers in NumPy"
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{
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"cell_type": "markdown",
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"source": [
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"This is just a quick overview of how to deal with missing values (i.e., \"NaN\"s for \"Not-a-Number\") in NumPy and I am happy to expand it over time. Yes, and there will also be a separate one for pandas some time!\n",
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"\n",
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"I would be happy to hear your comments and suggestions. \n",
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||||
"Please feel free to drop me a note via\n",
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||||
"[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/+SebastianRaschka).\n",
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"<hr>"
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]
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{
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"cell_type": "heading",
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"Sections"
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]
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{
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"cell_type": "markdown",
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"metadata": {},
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"- [Sample data from a CSV file](#Sample-data-from-a-CSV-file)\n",
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"- [Determining if a value is missing](#Determining-if-a-value-is-missing)\n",
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"- [Counting the number of missing values](#Counting-the-number-of-missing-values)\n",
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"- [Calculating the sum of an array that contains NaNs](#Calculating the sum of an array that contains NaNs)\n",
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"- [Removing all rows that contain missing values](#Removing-all-rows-that-contain-missing-values)\n",
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"- [Convert missing values to 0](#Convert-missing-values-to-0)\n",
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"- [Converting certain numbers to NaN](#Converting-certain-numbers-to-NaN)\n",
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"- [Remove all missing elements from an array](#Remove-all-missing-elements-from-an-array)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<br>\n",
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"<br>"
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]
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},
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{
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"cell_type": "heading",
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"level": 2,
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"metadata": {},
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"source": [
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"Sample data from a CSV file"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"[[back to top](#Sections)]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Let's assume that we have a CSV file with missing elements like the one shown below."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"<br>"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"%%file example.csv\n",
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"1,2,3,4\n",
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"5,6,,8\n",
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"10,11,12,"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"Overwriting example.csv\n"
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]
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}
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],
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"prompt_number": 3
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The `np.genfromtxt` function has a `missing_values` parameters which translates missing values into `np.nan` objects by default. This allows us to construct a new NumPy `ndarray` object, even if elements are missing."
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]
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},
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"\n",
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"<br>"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"import numpy as np\n",
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"ary = np.genfromtxt('./example.csv', delimiter=',')\n",
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"\n",
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"print('%s x %s array:\\n' %(ary.shape[0], ary.shape[1]))\n",
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"print(ary)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"3 x 4 array:\n",
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"\n",
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"[[ 1. 2. 3. 4.]\n",
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" [ 5. 6. nan 8.]\n",
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" [ 10. 11. 12. nan]]\n"
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]
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"<br>\n",
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"<br>"
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]
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},
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{
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"cell_type": "heading",
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"level": 2,
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"metadata": {},
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"source": [
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"Determining if a value is missing"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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||||
"source": [
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"[[back to top](#Sections)]"
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||||
]
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||||
},
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||||
{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"A handy function to test whether a value is a `NaN` or not is to use the `np.isnan` function."
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]
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"np.isnan(np.nan)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"text": [
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"True"
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{
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"source": [
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"<br>"
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"It is especially useful to create boolean masks for the so-called \"fancy indexing\" of NumPy arrays, which we will come back to later."
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]
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},
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{
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"np.isnan(ary)"
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"array([[False, False, False, False],\n",
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" [False, False, True, False],\n",
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" [False, False, False, True]], dtype=bool)"
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]
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"source": [
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"<br>\n",
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||||
"<br>"
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||||
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||||
{
|
||||
"cell_type": "heading",
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||||
"level": 2,
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||||
"metadata": {},
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||||
"source": [
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"Counting the number of missing values"
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]
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},
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{
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||||
"cell_type": "markdown",
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||||
"metadata": {},
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"source": [
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||||
"[[back to top](#Sections)]"
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]
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In order to find out how many elements are missing in our array, we can use the `np.isnan` function that we have seen in the previous section. "
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]
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{
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"cell_type": "code",
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"input": [
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"np.count_nonzero(np.isnan(ary))"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 8,
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"text": [
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"2"
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]
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}
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"prompt_number": 8
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<br>\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"If we want to determine the number of non-missing elements, we can simply revert the returned `Boolean` mask via the handy \"tilde\" sign."
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"np.count_nonzero(~np.isnan(ary))"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 9,
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"text": [
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"10"
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]
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}
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"prompt_number": 9
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<br>\n",
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"<br>"
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]
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},
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{
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"cell_type": "heading",
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"level": 2,
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||||
"metadata": {},
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||||
"source": [
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"Calculating the sum of an array that contains `NaN`s"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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||||
"[[back to top](#Sections)]"
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},
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{
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"cell_type": "markdown",
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||||
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"source": [
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"As we will find out via the following code snippet, we can't use NumPy's regular `sum` function to calculate the sum of an array."
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]
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{
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"cell_type": "code",
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||||
"collapsed": false,
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||||
"input": [
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"np.sum(ary)"
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],
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||||
"language": "python",
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||||
"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 10,
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"text": [
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"nan"
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]
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}
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],
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"prompt_number": 10
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||||
},
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||||
{
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"cell_type": "markdown",
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"metadata": {},
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"<br>"
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||||
]
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||||
},
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{
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"cell_type": "markdown",
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"metadata": {},
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||||
"source": [
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"Since the `np.sum` function does not work, use `np.nansum` instead:"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"print('total sum:', np.nansum(ary))"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"total sum: 62.0\n"
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]
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}
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],
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"prompt_number": 11
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"print('row sums:', np.nansum(ary, axis=0))"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"row sums: [ 16. 19. 15. 12.]\n"
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]
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}
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],
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"prompt_number": 12
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"print('column sums:', np.nansum(ary, axis=1))"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"column sums: [ 10. 19. 33.]\n"
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]
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}
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],
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"prompt_number": 13
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||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
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||||
"source": [
|
||||
"<br>\n",
|
||||
"<br>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "heading",
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||||
"level": 2,
|
||||
"metadata": {},
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||||
"source": [
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||||
"Removing all rows that contain missing values"
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]
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||||
},
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||||
{
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||||
"cell_type": "markdown",
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||||
"metadata": {},
|
||||
"source": [
|
||||
"[[back to top](#Sections)]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
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||||
"source": [
|
||||
"Here, we will use the `Boolean mask` again to return only those rows that DON'T contain missing values. And if we want to get only the rows that contain `NaN`s, we could simply drop the `~`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"ary[~np.isnan(ary).any(1)]"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "pyout",
|
||||
"prompt_number": 14,
|
||||
"text": [
|
||||
"array([[ 1., 2., 3., 4.]])"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 14
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<br>\n",
|
||||
"<br>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "heading",
|
||||
"level": 2,
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Convert missing values to 0"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"[[back to top](#Sections)]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Certain operations, algorithms, and other analyses might not work with `NaN` objects in our data array. But that's not a problem: We can use the convenient `np.nan_to_num` function will convert it to the value 0."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<br>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"ary0 = np.nan_to_num(ary)\n",
|
||||
"ary0"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "pyout",
|
||||
"prompt_number": 15,
|
||||
"text": [
|
||||
"array([[ 1., 2., 3., 4.],\n",
|
||||
" [ 5., 6., 0., 8.],\n",
|
||||
" [ 10., 11., 12., 0.]])"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 15
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<br>\n",
|
||||
"<br>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "heading",
|
||||
"level": 2,
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Converting certain numbers to NaN"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"[[back to top](#Sections)]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Vice versa, we can also convert any number to a `np.NaN` object. Here, we use the array that we created in the previous section and convert the `0`s back to `np.nan` objects."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<br>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"ary0[ary0==0] = np.nan\n",
|
||||
"ary0"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "pyout",
|
||||
"prompt_number": 16,
|
||||
"text": [
|
||||
"array([[ 1., 2., 3., 4.],\n",
|
||||
" [ 5., 6., nan, 8.],\n",
|
||||
" [ 10., 11., 12., nan]])"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 16
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<br>\n",
|
||||
"<br>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "heading",
|
||||
"level": 2,
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Remove all missing elements from an array"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"[[back to top](#Sections)]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This is one is a little bit more tricky. We can remove missing values via a combination of the `Boolean` mask and fancy indexing, however, this will have the disadvantage that it will flatten our array (we can't just punch holes into a NumPy array)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"ary[~np.isnan(ary)]"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "pyout",
|
||||
"prompt_number": 17,
|
||||
"text": [
|
||||
"array([ 1., 2., 3., 4., 5., 6., 8., 10., 11., 12.])"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 17
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Thus, this is a method that would better work on individual rows:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"x = np.array([1,2,np.nan])\n",
|
||||
"\n",
|
||||
"x[~np.isnan(np.array(x))]"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "pyout",
|
||||
"prompt_number": 21,
|
||||
"text": [
|
||||
"array([ 1., 2.])"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 21
|
||||
}
|
||||
],
|
||||
"metadata": {}
|
||||
}
|
||||
]
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user