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| − | | + | ~ Migrated |
| − | <br />
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| − | ==NumPy==
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| − | | |
| − | *NumPy (or Numpy) is a Linear Algebra Library for Python, the reason it is so important for Data Science with Python is that almost all of the libraries in the PyData Ecosystem rely on NumPy as one of their main building blocks.
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| − | | |
| − | *Numpy is also incredibly fast, as it has bindings to C libraries. For more info on why you would want to use Arrays instead of lists, check out this great [StackOverflow post](http://stackoverflow.com/questions/993984/why-numpy-instead-of-python-lists).
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| − | | |
| − | | |
| − | <br />
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| − | ===Installation===
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| − | ...
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| − | | |
| − | | |
| − | <br />
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| − | ===Arrays===
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| − | {| class="wikitable" style="width: 100%;"
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| − | ! colspan="2" rowspan="2" |
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| − | ! colspan="2" rowspan="2" |Method/Operation
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| − | ! rowspan="2" |Description/Comments
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| − | !Example
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| − | |-
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| − | !<syntaxhighlight lang="python3">
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| − | import numpy as np
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| − | </syntaxhighlight>
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| − | |-
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| − | ! rowspan="10" style="vertical-align:top;" |<h5 style="text-align:left">Methods for creating NumPy Arrays</h5>
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| − | | style="vertical-align:top;" |<h5 style="text-align:left">From a Python List</h5>
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| − | | colspan="2" |'''''<code>array()</code>'''''
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| − | |We can create an array by directly converting a list or list of lists.
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| − | |<code>my_list = [1,2,3]</code>
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| − | <code>np.array(my_list)</code>
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| − | | |
| − | | |
| − | <code>my_matrix = [[1,2,3],[4,5,6],[7,8,9]]</code>
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| − | | |
| − | <code>np.array(my_matrix)</code>
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| − | |-
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| − | | rowspan="9" style="vertical-align:top;" |<h5 style="text-align:left">From Built-in NumPy Methods</h5>
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| − | | colspan="2" |'''''<code>arange()</code>'''''
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| − | |Return evenly spaced values within a given interval.
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| − | |<code>np.arange(0,10)</code>
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| − | <code>np.arange(0,11,2)</code>
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| − | |-
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| − | | colspan="2" |'''''<code>zeros()</code>'''''
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| − | |Generate arrays of zeros.
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| − | |<code>np.zeros(3)</code>
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| − | <code>np.zeros((5,5))</code>
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| − | |-
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| − | | colspan="2" |'''''<code>ones()</code>'''''
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| − | |Generate arrays of ones.
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| − | |<code>np.ones(3)</code>
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| − | <code>np.ones((3,3))</code>
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| − | |-
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| − | | colspan="2" |'''''<code>linspace()</code>'''''
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| − | |Return evenly spaced numbers over a specified interval.
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| − | |<code>np.linspace(0,10,3)</code>
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| − | <code>np.linspace(0,10,50)</code>
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| − | |-
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| − | | colspan="2" |'''''<code>eye()</code>'''''
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| − | |Creates an identity matrix.
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| − | |<code>np.linspace(0,10,50)</code>
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| − | |-
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| − | | rowspan="4" |'''''<code>random</code>'''''
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| − | |'''''<code>rand()</code>'''''
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| − | |Create an array of the given shape and populate it with random samples from a uniform distribution over <code>[0, 1)</code>.
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| − | |<syntaxhighlight lang="python3">
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| − | np.random.rand(2)
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| − | np.random.rand(5,5)
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| − | | |
| − | | |
| − | # Another way to invoke a function:
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| − | from numpy.random import rand
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| − | # Then you can call the function directly
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| − | rand(5,5)
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| − | </syntaxhighlight><br />
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| − | |-
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| − | |'''''<code>randn()</code>'''''
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| − | |Return a sample (or samples) from the "standard normal" distribution. Unlike rand which is uniform.
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| − | |<code>np.random.randn(2)</code>
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| − | <code>np.random.randn(5,5)</code>
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| − | |-
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| − | |'''''<code>randint()</code>'''''
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| − | |Return random integers from <code>low</code> (inclusive) to <code>high</code> (exclusive).
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| − | |<code>np.random.randint(1,100)</code>
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| − | <code>np.random.randint(1,100,10)</code>
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| − | |-
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| − | |'''<code>seed()</code>'''
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| − | |sets the random seed of the NumPy pseudo-random number generator. It provides an essential input that enables NumPy to generate pseudo-random numbers for random processes. See [[wikipedia:Random_seed|s1]] and [https://www.sharpsightlabs.com/blog/numpy-random-seed/ s2]. for explanation.
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| − | |<code>np.random.seed(101)</code>
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| − | |-
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| − | ! rowspan="4" style="vertical-align:top;" |<h5 style="text-align:left">Others Array Attributes and Methods</h5>
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| − | | rowspan="4" |
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| − | | colspan="2" |''<code>'''reshape()'''</code>''
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| − | |Returns an array containing the same data with a new shape.
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| − | |<code>arr.reshape(5,5)</code>
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| − | |-
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| − | | colspan="2" |'''''<code>max()</code>, <code>min()</code>, <code>argmax()</code>, <code>argmin()</code>'''''
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| − | |Finding max or min values. Or to find their index locations using argmin or argmax.
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| − | |<code>arr.max()</code>
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| − | <code>arr.argmax()</code>
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| − | |-
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| − | | colspan="2" |''<code>'''shape()'''</code>''
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| − | |Shape is an attribute that arrays have (not a method).
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| − | |NO LO ENTENDI.. REVISAR!
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| − | | |
| − | | |
| − | <nowiki>#</nowiki>Length of array
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| − | | |
| − | arr_length = arr2d.shape[1]
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| − | <br />
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| − | |-
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| − | | colspan="2" |''<code>'''dtype()'''</code>''
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| − | |You can also grab the data type of the object in the array.
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| − | |<code>arr.dtype</code>
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| − | |-
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| − | !<nowiki>-</nowiki>
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| − | !-
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| − | ! colspan="2" |-
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| − | !-
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| − | !-
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| − | |-
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| − | ! rowspan="8" style="vertical-align:top;" |<h5 style="text-align:left">Indexing and Selection</h5>
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| − | | |
| − | <div style="text-align:left">
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| − | *How to select elements or groups of elements from an array.
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| − | *The general format is '''arr_2d[row][col]''' or '''arr_2d[row,col]'''. I recommend usually using the comma notation for clarity.
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| − | </div>
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| − | |
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| − | | colspan="2" |
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| − | | colspan="2" |<div class="mw-collapsible mw-collapsed" style="">
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| − | '''Creating sample array for the following examples:'''
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| − | <div class="mw-collapsible-content">
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| − | <syntaxhighlight lang="python3">
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| − | import numpy as np
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| − | arr = np.arange(0,10)
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| − | # 1D Array:
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| − | arr = np.arange(0,11)
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| − | #Show
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| − | arr
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| − | Output: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
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| − | | |
| − | # 2D Array
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| − | arr_2d = np.array(([5,10,15],[20,25,30],[35,40,45]))
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| − | #Show
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| − | arr_2d
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| − | Output:
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| − | array([[ 5, 10, 15],
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| − | [20, 25, 30],
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| − | [35, 40, 45]])
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| − | </syntaxhighlight>
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| − | </div>
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| − | </div>
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| − | |-
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| − | | rowspan="2" style="vertical-align:top;" |<h5 style="text-align:left">Bracket Indexing and Selection (Slicing)</h5>
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| − | | colspan="2" |
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| − | |Note: When we create a sub-array slicing an array (slice_of_arr = arr[0:6]), data is not copied, it's a view of the original array! This avoids memory problems! To get a copy, need to use the method '''copy()'''. See important note below.
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| − | |<syntaxhighlight lang="python3">
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| − | #Get a value at an index
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| − | arr[8]
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| − | | |
| − | #Get values in a range
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| − | arr[1:5]
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| − | | |
| − | slice_of_arr = arr[0:6]
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| − | | |
| − | #2D
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| − | arr_2d[1]
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| − | arr_2d[1][0]
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| − | arr_2d[1,0] # The same that above
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| − | | |
| − | #Shape (2,2) from top right corner
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| − | arr_2d[:2,1:]
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| − | #Output:
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| − | array([[10, 15],
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| − | [25, 30]])
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| − | | |
| − | #Shape bottom row
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| − | arr_2d[2,:]
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| − | </syntaxhighlight><br />
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| − | |-
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| − | | colspan="2" |
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| − | | colspan="2" |<div class="mw-collapsible mw-collapsed" style="">
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| − | '''Fancy Indexing''':
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| − | <div class="mw-collapsible-content">
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| − | Fancy indexing allows you to select entire rows or columns out of order.
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| − | | |
| − | Example:<syntaxhighlight lang="python3">
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| − | # Set up matrix
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| − | arr2d = np.zeros((10,10))
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| − | | |
| − | # Length of array
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| − | arr_length = arr2d.shape[1]
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| − | | |
| − | # Set up array
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| − | for i in range(arr_length):
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| − | arr2d[i] = i
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| − |
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| − | arr2d
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| − | # Output:
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| − | array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
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| − | [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
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| − | [2., 2., 2., 2., 2., 2., 2., 2., 2., 2.],
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| − | [3., 3., 3., 3., 3., 3., 3., 3., 3., 3.],
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| − | [4., 4., 4., 4., 4., 4., 4., 4., 4., 4.],
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| − | [5., 5., 5., 5., 5., 5., 5., 5., 5., 5.],
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| − | [6., 6., 6., 6., 6., 6., 6., 6., 6., 6.],
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| − | [7., 7., 7., 7., 7., 7., 7., 7., 7., 7.],
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| − | [8., 8., 8., 8., 8., 8., 8., 8., 8., 8.],
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| − | [9., 9., 9., 9., 9., 9., 9., 9., 9., 9.]])
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| − | | |
| − | # Fancy indexing allows the following
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| − | arr2d[[6,4,2,7]]
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| − | # Output:
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| − | array([[6., 6., 6., 6., 6., 6., 6., 6., 6., 6.],
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| − | [4., 4., 4., 4., 4., 4., 4., 4., 4., 4.],
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| − | [2., 2., 2., 2., 2., 2., 2., 2., 2., 2.],
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| − | [7., 7., 7., 7., 7., 7., 7., 7., 7., 7.]])
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| − | </syntaxhighlight><br />
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| − | </div>
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| − | </div>
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| − | |-
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| − | | rowspan="2" style="vertical-align:top;" |<h5 style="text-align:left">Broadcasting</h5>
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| − | | |
| − | | |
| − | (Setting a value with index range)
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| − | | colspan="2" rowspan="2" |
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| − | | rowspan="2" |Setting a value with index range:
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| − | Numpy arrays differ from a normal Python list because of their ability to broadcast.
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| − | |arr[0:5]=100<br />'''#'''Show
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| − | arr
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| − | | |
| − | Output: array([100, 100, 100, 100, 100, 5, 6, 7, 8, 9, 10])
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| − | |-
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| − | |'''#'''Setting all the values of an Array
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| − | arr[:]=99
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| − | |-
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| − | |style="vertical-align:top;"|<h5 style="text-align:left">Get a copy of an Array</h5>
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| − | | colspan="2" |'''<code>copy''()''</code>'''
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| − | |Note: When we create a sub-array slicing an array (slice_of_arr = arr[0:6]), data is not copied, it's a view of the original array! This avoids memory problems! To get a copy, need to use the method '''copy()'''. See important note below.
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| − | |arr_copy = arr.copy()
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| − | |-
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| − | |style="vertical-align:top;"|<h5 style="text-align:left">Important notes on Slices</h5>
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| − | | colspan="2" |
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| − | | colspan="2" |<div class="mw-collapsible mw-collapsed" style=""><syntaxhighlight lang="python3">
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| − | slice_of_arr = arr[0:6]
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| − | #Show slice
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| − | slice_of_arr
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| − | Output: array([0, 1, 2, 3, 4, 5])
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| − | | |
| − | #Making changes in slice_of_arr
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| − | slice_of_arr[:]=99
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| − | #Show slice
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| − | slice_of_arr
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| − | Output: array([99, 99, 99, 99, 99, 99])
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| − | | |
| − | #Now note the changes also occur in our original array!
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| − | #Show
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| − | arr
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| − | Output: array([99, 99, 99, 99, 99, 99, 6, 7, 8, 9, 10])
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| − | | |
| − | #When we create a sub-array slicing an array (slice_of_arr = arr[0:6]), data is not copied, it's a view of the original array! This avoids memory problems!
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| − | | |
| − | #To get a copy, need to use the method copy()
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| − | </syntaxhighlight>
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| − | </div>
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| − | |-
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| − | |style="vertical-align:top;"|<h5 style="text-align:left">Using brackets for selection based on comparison operators and booleans</h5>
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| − | | colspan="2" |
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| − | | colspan="2" |<div class="mw-collapsible mw-collapsed" style=""><syntaxhighlight lang="python3">
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| − | arr = np.arange(1,11)
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| − | arr > 4
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| − | # Output:
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| − | array([False, False, False, False, True, True, True, True, True,
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| − | True])
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| − | | |
| − | bool_arr = arr>4
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| − | bool_arr
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| − | # Output:
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| − | array([False, False, False, False, True, True, True, True, True,
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| − | True])
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| − | | |
| − | arr[bool_arr]
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| − | # Output:
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| − | array([ 5, 6, 7, 8, 9, 10])
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| − | | |
| − | arr[arr>2]
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| − | # Output:
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| − | array([ 3, 4, 5, 6, 7, 8, 9, 10])
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| − | | |
| − | x = 2
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| − | arr[arr>x]
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| − | # Output:
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| − | array([ 3, 4, 5, 6, 7, 8, 9, 10])
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| − | </syntaxhighlight>
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| − | </div>
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| − | |-
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| − | !-
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| − | !-
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| − | ! colspan="2" |-
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| − | !-
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| − | !-
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| − | |-
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| − | !style="vertical-align:top;"|<h5 style="text-align:left">Arithmetic operations</h5>
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| − | |
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| − | | colspan="2" |<code>arr + arr</code>
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| − | <code>arr - arr</code>
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| − | | |
| − | <code>arr * arr</code>
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| − | | |
| − | <code>arr/arr</code>
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| − | | |
| − | <code>1/arr</code>
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| − | | |
| − | <code>arr**3</code>
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| − | |Warning on division by zero, but not an error!
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| − | <code>0/0 -> nan</code>
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| − | | |
| − | <code>1/0 -> inf</code>
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| − | |<syntaxhighlight lang="python3">
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| − | import numpy as np
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| − | arr = np.arange(0,10)
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| − | | |
| − | arr + arr
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| − | # Output:
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| − | array([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 18])
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| − | | |
| − | arr**3
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| − | # Output:
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| − | array([ 0, 1, 8, 27, 64, 125, 216, 343, 512, 729])
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| − | </syntaxhighlight>
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| − | |-
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| − | ! rowspan="5" style="vertical-align:top;" |<h5 style="text-align:left">[https://docs.scipy.org/doc/numpy/reference/ufuncs.html Universal Array Functions]</h5>
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| − | | rowspan="5" |
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| − | | colspan="2" |<code>np.sqrt(arr)</code>
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| − | |Taking Square Roots
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| − | | rowspan="5" |<syntaxhighlight lang="python3">
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| − | np.sin(arr)
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| − | # Output:
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| − | array([ 0. , 0.84147098, 0.90929743, 0.14112001, -0.7568025 ,
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| − | -0.95892427, -0.2794155 , 0.6569866 , 0.98935825, 0.41211849])
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| − | </syntaxhighlight>
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| − | |-
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| − | | colspan="2" |<code>np.exp(arr)</code>
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| − | |Calcualting exponential (e^)
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| − | |-
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| − | | colspan="2" |<code>np.max(arr)</code>
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| − | same as <code>arr.max()</code>
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| − | |Max
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| − | |-
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| − | | colspan="2" |<code>np.sin(arr)</code>
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| − | |Sin
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| − | |-
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| − | | colspan="2" |<code>np.log(arr)</code>
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| − | |Natural logarithm
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| − | |}
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| − | | |
| − | | |
| − | <br />
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| − | | |
| − | ==Pandas==
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| − | You can think of pandas as an extremely powerful version of Excel, with a lot more features. In this section of the course, you should go through the notebooks in this order:
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| − | | |
| − | | |
| − | <br />
| |
| − | ===Series===
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| − | A Series is very similar to a NumPy array (in fact it is built on top of the NumPy array object). What differentiates the NumPy array from a Series, is that a Series can have axis labels, meaning it can be indexed by a label, instead of just a number location. It also doesn't need to hold numeric data, it can hold any arbitrary Python Object.
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| − | | |
| − | {| class="wikitable" style="width: 100%;"
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| − | ! rowspan="2" |
| |
| − | ! rowspan="2" |
| |
| − | ! rowspan="2" |Method/Operator
| |
| − | ! rowspan="2" |Description/Comments
| |
| − | !Example
| |
| − | |-
| |
| − | !<syntaxhighlight lang="python3">
| |
| − | import pandas as pd
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| − | </syntaxhighlight>
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| − | |-
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| − | ! rowspan="3" style="vertical-align:top;" |<h4 style="text-align:left">Creating Pandas Series</h4>
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| − | | |
| − | | |
| − | <div style="text-align:left">
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| − | You can convert a <code>list</code>, <code>numpy array</code>, or <code>dictionary</code> to a Series.
| |
| − | </div>
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| − | |style="vertical-align:top;"|<h5 style="text-align:left">From a List</h5>
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| − | |<code>pd.Series(my_list)</code>
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| − | | colspan="2" rowspan="3" |<syntaxhighlight lang="python3">
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| − | # Creating some test data:
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| − | labels = ['a','b','c']
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| − | my_list = [10,20,30]
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| − | arr = np.array([10,20,30])
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| − | d = {'a':10,'b':20,'c':30}
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| − | | |
| − | | |
| − | pd.Series(data=my_list)
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| − | pd.Series(my_list)
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| − | pd.Series(arr)
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| − | # Output:
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| − | 0 10
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| − | 1 20
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| − | 2 30
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| − | dtype: int64
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| − | | |
| − | pd.Series(data=my_list,index=labels)
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| − | pd.Series(my_list,labels)
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| − | pd.Series(arr,labels)
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| − | pd.Series(d)
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| − | # Output:
| |
| − | a 10
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| − | b 20
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| − | c 30
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| − | dtype: int64
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | |style="vertical-align:top;"|<h5 style="text-align:left">From a NumPy Array</h5>
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| − | |<code>pd.Series(arr)</code>
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| − | |-
| |
| − | |style="vertical-align:top;"|<h5 style="text-align:left">From a Dectionary</h5>
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| − | |<code>pd.Series(d)</code>
| |
| − | |-
| |
| − | !style="vertical-align:top;"|<h4 style="text-align:left">Data in a Series</h4>
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| − | | |
| − | |
| |
| − | |
| |
| − | | colspan="2" |A pandas Series can hold a variety of object types. Even functions (although unlikely that you will use this)<syntaxhighlight lang="python3">
| |
| − | pd.Series(data=labels)
| |
| − | # Output:
| |
| − | 0 a
| |
| − | 1 b
| |
| − | 2 c
| |
| − | dtype: object
| |
| − | | |
| − | # Holding «functions» into a Series
| |
| − | # Output:
| |
| − | pd.Series([sum,print,len])
| |
| − | 0 <built-in function sum>
| |
| − | 1 <built-in function print>
| |
| − | 2 <built-in function len>
| |
| − | dtype: object
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | !style="vertical-align:top;"|<h4 style="text-align:left">Index in Series</h4>
| |
| − | |
| |
| − | |
| |
| − | | colspan="2" |The key to using a Series is understanding its index. Pandas makes use of these index names or numbers by allowing for fast look ups of information (works like a hash table or dictionary).<syntaxhighlight lang="python3">
| |
| − | ser1 = pd.Series([1,2,3,4],index = ['USA', 'Germany','USSR', 'Japan'])
| |
| − | ser1
| |
| − | # Output:
| |
| − | USA 1
| |
| − | Germany 2
| |
| − | USSR 3
| |
| − | Japan 4
| |
| − | dtype: int64
| |
| − | | |
| − | ser2 = pd.Series([1,2,5,4],index = ['USA', 'Germany','Italy', 'Japan'])
| |
| − | | |
| − | ser1['USA']
| |
| − | # Output:
| |
| − | 1
| |
| − | | |
| − | # Operations are then also done based off of index:
| |
| − | ser1 + ser2
| |
| − | # Output:
| |
| − | Germany 4.0
| |
| − | Italy NaN
| |
| − | Japan 8.0
| |
| − | USA 2.0
| |
| − | USSR NaN
| |
| − | dtype: float64
| |
| − | </syntaxhighlight>
| |
| − | |}
| |
| − | | |
| − | | |
| − | <br />
| |
| − | | |
| − | ===DataFrames===
| |
| − | DataFrames are the workhorse of pandas and are directly inspired by the R programming language. We can think of a DataFrame as a bunch of Series objects put together to share the same index. Let's use pandas to explore this topic!
| |
| − | | |
| − | | |
| − | <syntaxhighlight lang="python">
| |
| − | import pandas as pd
| |
| − | import numpy as np
| |
| − | | |
| − | from numpy.random import randn
| |
| − | np.random.seed(101)
| |
| − | | |
| − | df = pd.DataFrame(randn(5,4),index='A B C D E'.split(),columns='W X Y Z'.split())
| |
| − | | |
| − | df
| |
| − | # Output:
| |
| − | W X Y Z
| |
| − | A 2.706850 0.628133 0.907969 0.503826
| |
| − | B 0.651118 -0.319318 -0.848077 0.605965
| |
| − | C -2.018168 0.740122 0.528813 -0.589001
| |
| − | D 0.188695 -0.758872 -0.933237 0.955057
| |
| − | E 0.190794 1.978757 2.605967 0.683509
| |
| − | </syntaxhighlight>
| |
| − | | |
| − | | |
| − | | |
| − | '''DataFrame Columns are just Series:'''<syntaxhighlight lang="python3">
| |
| − | type(df['W'])
| |
| − | # Output:
| |
| − | pandas.core.series.Series
| |
| − | </syntaxhighlight>
| |
| − | {| class="wikitable" style="width: 100%;"
| |
| − | !
| |
| − | !
| |
| − | !Method/
| |
| − | Operator
| |
| − | !Description/Comments
| |
| − | !Example
| |
| − | |-
| |
| − | ! rowspan="5" style="vertical-align:top;" |<h4 style="text-align:left">Selection and Indexing</h4>
| |
| − | | |
| − | | |
| − | <div style="text-align:left">
| |
| − | Let's learn the various
| |
| − | | |
| − | methods to grab data
| |
| − | | |
| − | from a DataFrame
| |
| − | </div>
| |
| − | | |
| − | |style="vertical-align:top;"|<h5 style="text-align:left">Standard systax</h5>
| |
| − | |<code>'''df[<nowiki>''</nowiki>]'''</code>
| |
| − | |
| |
| − | | rowspan="2" |<syntaxhighlight lang="python3">
| |
| − | # Pass a list of column names:
| |
| − | df[['W','Z']]
| |
| − | | |
| − | W Z
| |
| − | A 2.706850 0.503826
| |
| − | B 0.651118 0.605965
| |
| − | C -2.018168 -0.589001
| |
| − | D 0.188695 0.955057
| |
| − | E 0.190794 0.683509
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | |style="vertical-align:top;"|<h5 style="text-align:left">SQL syntax</h5>
| |
| − | (NOT RECOMMENDED!)
| |
| − | |<code>'''df.W'''</code>
| |
| − | |
| |
| − | |-
| |
| − | |style="vertical-align:top;"|<h5 style="text-align:left">Selecting Rows</h5>
| |
| − | |'''<code>df.loc[<nowiki>''</nowiki>]</code>'''
| |
| − | |
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | df.loc['A']
| |
| − | # Or select based off of position instead of label :
| |
| − | df.iloc[2]
| |
| − | # Output:
| |
| − | W 2.706850
| |
| − | X 0.628133
| |
| − | Y 0.907969
| |
| − | Z 0.503826
| |
| − | Name: A, dtype: float64
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | |style="vertical-align:top;"|<h5 style="text-align:left">Selecting subset of rows and columns</h5>
| |
| − | |'''<code>df.loc[<nowiki>''</nowiki>,<nowiki>''</nowiki>]</code>'''
| |
| − | |
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | df.loc['B','Y']
| |
| − | # Output:
| |
| − | -0.84807698340363147
| |
| − | | |
| − | df.loc[['A','B'],['W','Y']]
| |
| − | # Output:
| |
| − | W Y
| |
| − | A 2.706850 0.907969
| |
| − | B 0.651118 -0.848077
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | |style="vertical-align:top;"|<h5 style="text-align:left">Conditional Selection</h5>
| |
| − | |
| |
| − | | colspan="2" |<div class="mw-collapsible mw-collapsed" style="">
| |
| − | An important feature of pandas is conditional selection using bracket notation, very similar to numpy:
| |
| − | <div class="mw-collapsible-content">
| |
| − | <syntaxhighlight lang="python3">
| |
| − | df
| |
| − | # Output:
| |
| − | W X Y Z
| |
| − | A 2.706850 0.628133 0.907969 0.503826
| |
| − | B 0.651118 -0.319318 -0.848077 0.605965
| |
| − | C -2.018168 0.740122 0.528813 -0.589001
| |
| − | D 0.188695 -0.758872 -0.933237 0.955057
| |
| − | E 0.190794 1.978757 2.605967 0.683509
| |
| − | | |
| − | df>0
| |
| − | # Output:
| |
| − | W X Y Z
| |
| − | A True True True True
| |
| − | B True False False True
| |
| − | C False True True False
| |
| − | D True False False True
| |
| − | E True True True True
| |
| − | | |
| − | df[df>0]
| |
| − | # Output:
| |
| − | W X Y Z
| |
| − | A 2.706850 0.628133 0.907969 0.503826
| |
| − | B 0.651118 NaN NaN 0.605965
| |
| − | C NaN 0.740122 0.528813 NaN
| |
| − | D 0.188695 NaN NaN 0.955057
| |
| − | E 0.190794 1.978757 2.605967 0.683509
| |
| − | | |
| − | df[df['W']>0]
| |
| − | # Output:
| |
| − | W X Y Z
| |
| − | A 2.706850 0.628133 0.907969 0.503826
| |
| − | B 0.651118 -0.319318 -0.848077 0.605965
| |
| − | D 0.188695 -0.758872 -0.933237 0.955057
| |
| − | E 0.190794 1.978757 2.605967 0.683509
| |
| − | | |
| − | df[df['W']>0]['Y']
| |
| − | # Output:
| |
| − | A 0.907969
| |
| − | B -0.848077
| |
| − | D -0.933237
| |
| − | E 2.605967
| |
| − | Name: Y, dtype: float64
| |
| − | | |
| − | df[df['W']>0][['Y','X']]
| |
| − | # Output:
| |
| − | Y X
| |
| − | A 0.907969 0.628133
| |
| − | B -0.848077 -0.319318
| |
| − | D -0.933237 -0.758872
| |
| − | E 2.605967 1.978757
| |
| − | | |
| − | # For two conditions you can use | and & with parenthesis:
| |
| − | df[(df['W']>0) & (df['Y'] > 1)]
| |
| − | # Output:
| |
| − | W X Y Z
| |
| − | E 0.190794 1.978757 2.605967 0.683509
| |
| − | </syntaxhighlight>
| |
| − | </div>
| |
| − | </div>
| |
| − | |-
| |
| − | !style="vertical-align:top;"|<h4 style="text-align:left">Creating a new column</h4>
| |
| − | |
| |
| − | |
| |
| − | |
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | df['new'] = df['W'] + df['Y']
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | !style="vertical-align:top;"|<h4 style="text-align:left">Removing Columns</h4>
| |
| − | |
| |
| − | |'''<code>df.drop()</code>'''
| |
| − | | colspan="2" |
| |
| − | <div class="mw-collapsible mw-collapsed" style="">
| |
| − | <syntaxhighlight lang="python3">
| |
| − | df.drop('new',axis=1)
| |
| − | # Output:
| |
| − | W X Y Z
| |
| − | A 2.706850 0.628133 0.907969 0.503826
| |
| − | B 0.651118 -0.319318 -0.848077 0.605965
| |
| − | C -2.018168 0.740122 0.528813 -0.589001
| |
| − | D 0.188695 -0.758872 -0.933237 0.955057
| |
| − | E 0.190794 1.978757 2.605967 0.683509
| |
| − | | |
| − | # Not inplace unless specified!
| |
| − | df
| |
| − | # Output:
| |
| − | W X Y Z new
| |
| − | A 2.706850 0.628133 0.907969 0.503826 3.614819
| |
| − | B 0.651118 -0.319318 -0.848077 0.605965 -0.196959
| |
| − | C -2.018168 0.740122 0.528813 -0.589001 -1.489355
| |
| − | D 0.188695 -0.758872 -0.933237 0.955057 -0.744542
| |
| − | E 0.190794 1.978757 2.605967 0.683509 2.796762
| |
| − | | |
| − | df.drop('new',axis=1,inplace=True)
| |
| − | df
| |
| − | # Output:
| |
| − | W X Y Z
| |
| − | A 2.706850 0.628133 0.907969 0.503826
| |
| − | B 0.651118 -0.319318 -0.848077 0.605965
| |
| − | C -2.018168 0.740122 0.528813 -0.589001
| |
| − | D 0.188695 -0.758872 -0.933237 0.955057
| |
| − | E 0.190794 1.978757 2.605967 0.683509
| |
| − | | |
| − | | |
| − | # Can also drop rows this way:
| |
| − | df.drop('E',axis=0,inplace=True)
| |
| − | # Output:
| |
| − | W X Y Z
| |
| − | A 2.706850 0.628133 0.907969 0.503826
| |
| − | B 0.651118 -0.319318 -0.848077 0.605965
| |
| − | C -2.018168 0.740122 0.528813 -0.589001
| |
| − | D 0.188695 -0.758872 -0.933237 0.955057
| |
| − | </syntaxhighlight>
| |
| − | </div>
| |
| − | |-
| |
| − | ! rowspan="2" style="vertical-align:top;" |<h4 style="text-align:left">Resetting the index</h4>
| |
| − | |style="vertical-align:top;"|<h5 style="text-align:left">Reset to default</h5>
| |
| − | (0,1...n index)
| |
| − | |'''<code>df.reset_index()</code>'''
| |
| − | | colspan="2" |<div class="mw-collapsible mw-collapsed" style="">
| |
| − | <syntaxhighlight lang="python3">
| |
| − | df
| |
| − | # Output:
| |
| − | W X Y Z
| |
| − | A 2.706850 0.628133 0.907969 0.503826
| |
| − | B 0.651118 -0.319318 -0.848077 0.605965
| |
| − | C -2.018168 0.740122 0.528813 -0.589001
| |
| − | D 0.188695 -0.758872 -0.933237 0.955057
| |
| − | E 0.190794 1.978757 2.605967 0.683509
| |
| − | | |
| − | df.reset_index()
| |
| − | # Output:
| |
| − | index W X Y Z
| |
| − | 0 A 2.706850 0.628133 0.907969 0.503826
| |
| − | 1 B 0.651118 -0.319318 -0.848077 0.605965
| |
| − | 2 C -2.018168 0.740122 0.528813 -0.589001
| |
| − | 3 D 0.188695 -0.758872 -0.933237 0.955057
| |
| − | 4 E 0.190794 1.978757 2.605967 0.683509
| |
| − | </syntaxhighlight>
| |
| − | </div>
| |
| − | |-
| |
| − | |style="vertical-align:top;"|<h5 style="text-align:left">Setting index to something else</h5>
| |
| − | |'''<code>df.set_index(<nowiki>''</nowiki>)</code>'''
| |
| − | | colspan="2" |<div class="mw-collapsible mw-collapsed" style="">
| |
| − | <syntaxhighlight lang="python3">
| |
| − | newind = 'CA NY WY OR CO'.split()
| |
| − | df['States'] = newind
| |
| − | | |
| − | df
| |
| − | # Output:
| |
| − | W X Y Z States
| |
| − | A 2.706850 0.628133 0.907969 0.503826 CA
| |
| − | B 0.651118 -0.319318 -0.848077 0.605965 NY
| |
| − | C -2.018168 0.740122 0.528813 -0.589001 WY
| |
| − | D 0.188695 -0.758872 -0.933237 0.955057 OR
| |
| − | E 0.190794 1.978757 2.605967 0.683509 CO
| |
| − | | |
| − | df.set_index('States')
| |
| − | # Output:
| |
| − | W X Y Z
| |
| − | States
| |
| − | CA 2.706850 0.628133 0.907969 0.503826
| |
| − | NY 0.651118 -0.319318 -0.848077 0.605965
| |
| − | WY -2.018168 0.740122 0.528813 -0.589001
| |
| − | OR 0.188695 -0.758872 -0.933237 0.955057
| |
| − | CO 0.190794 1.978757 2.605967 0.683509
| |
| − | | |
| − | df
| |
| − | # Output:
| |
| − | W X Y Z States
| |
| − | A 2.706850 0.628133 0.907969 0.503826 CA
| |
| − | B 0.651118 -0.319318 -0.848077 0.605965 NY
| |
| − | C -2.018168 0.740122 0.528813 -0.589001 WY
| |
| − | D 0.188695 -0.758872 -0.933237 0.955057 OR
| |
| − | E 0.190794 1.978757 2.605967 0.683509 CO
| |
| − | | |
| − | # We net to add «inplace=True»:
| |
| − | df.set_index('States',inplace=True)
| |
| − | df
| |
| − | # Output:
| |
| − | W X Y Z
| |
| − | States
| |
| − | CA 2.706850 0.628133 0.907969 0.503826
| |
| − | NY 0.651118 -0.319318 -0.848077 0.605965
| |
| − | WY -2.018168 0.740122 0.528813 -0.589001
| |
| − | OR 0.188695 -0.758872 -0.933237 0.955057
| |
| − | CO 0.190794 1.978757 2.605967 0.683509
| |
| − | </syntaxhighlight>
| |
| − | </div>
| |
| − | |-
| |
| − | ! rowspan="2" style="vertical-align:top;" |<h4 style="text-align:left">Multi-Indexed DataFrame</h4>
| |
| − | |style="vertical-align:top;"|<h5 style="text-align:left">Creating a Multi-Indexed DataFrame</h5>
| |
| − | |
| |
| − | | colspan="2" |<div class="mw-collapsible mw-collapsed" style="">
| |
| − | <syntaxhighlight lang="python3">
| |
| − | # Index Levels
| |
| − | outside = ['G1','G1','G1','G2','G2','G2']
| |
| − | inside = [1,2,3,1,2,3]
| |
| − | hier_index = list(zip(outside,inside))
| |
| − | hier_index = pd.MultiIndex.from_tuples(hier_index)
| |
| − | | |
| − | hier_index
| |
| − | # Output:
| |
| − | MultiIndex(levels=[['G1', 'G2'], [1, 2, 3]],
| |
| − | labels=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]])
| |
| − | | |
| − | df = pd.DataFrame(np.random.randn(6,2),index=hier_index,columns=['A','B'])
| |
| − | df
| |
| − | # Output:
| |
| − | A B
| |
| − | G1 1 0.153661 0.167638
| |
| − | 2 -0.765930 0.962299
| |
| − | 3 0.902826 -0.537909
| |
| − | G2 1 -1.549671 0.435253
| |
| − | 2 1.259904 -0.447898
| |
| − | 3 0.266207 0.412580
| |
| − | </syntaxhighlight>
| |
| − | </div>
| |
| − | |-
| |
| − | |style="vertical-align:top;"|<h5 style="text-align:left">Multi-Index and Index Hierarchy</h5>
| |
| − | |
| |
| − | | colspan="2" |<div class="mw-collapsible mw-collapsed" style="">
| |
| − | <syntaxhighlight lang="python3">
| |
| − | df.loc['G1']
| |
| − | # Output:
| |
| − | A B
| |
| − | 1 0.153661 0.167638
| |
| − | 2 -0.765930 0.962299
| |
| − | 3 0.902826 -0.537909
| |
| − | | |
| − | df.loc['G1'].loc[1]
| |
| − | # Output:
| |
| − | A 0.153661
| |
| − | B 0.167638
| |
| − | Name: 1, dtype: float64
| |
| − | | |
| − | df.index.names
| |
| − | # Output:
| |
| − | FrozenList([None, None])
| |
| − | | |
| − | df.index.names = ['Group','Num']
| |
| − | df
| |
| − | # Output:
| |
| − | A B
| |
| − | Group Num
| |
| − | G1 1 0.153661 0.167638
| |
| − | 2 -0.765930 0.962299
| |
| − | 3 0.902826 -0.537909
| |
| − | G2 1 -1.549671 0.435253
| |
| − | 2 1.259904 -0.447898
| |
| − | 3 0.266207 0.412580
| |
| − | | |
| − | df.xs('G1')
| |
| − | # Output:
| |
| − | A B
| |
| − | Num
| |
| − | 1 0.153661 0.167638
| |
| − | 2 -0.765930 0.962299
| |
| − | 3 0.902826 -0.537909
| |
| − | | |
| − | df.xs(['G1',1])
| |
| − | # Output:
| |
| − | A 0.153661
| |
| − | B 0.167638
| |
| − | Name: (G1, 1), dtype: float64
| |
| − | | |
| − | df.xs(1,level='Num')
| |
| − | # Output:
| |
| − | A B
| |
| − | Group
| |
| − | G1 0.153661 0.167638
| |
| − | G2 -1.549671 0.435253
| |
| − | </syntaxhighlight>
| |
| − | </div>
| |
| − | |}
| |
| − | | |
| − | | |
| − | | |
| − | | |
| − | | |
| − | <br />
| |
| − | | |
| − | ===Missing Data===
| |
| − | https://www.geeksforgeeks.org/python-pandas-dataframe-dropna/
| |
| − | | |
| − | Pandas will recognise a value as null if it is a np.nan object, which will print as NaN in the DataFrame.
| |
| − | | |
| − | Let's show a few convenient methods to deal with Missing Data in pandas.
| |
| − | | |
| − | *<code>dropna()</code> method allows the user to analyze and drop Rows/Columns with Null values in different ways:
| |
| − | <blockquote>
| |
| − | <syntaxhighlight lang="python">
| |
| − | DataFrameName.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)
| |
| − | </syntaxhighlight>
| |
| − | </blockquote>
| |
| − | | |
| − | *<code>fillna()</code> allows to fill Null fields with a given value:
| |
| − | | |
| − | | |
| − | <syntaxhighlight lang="python">
| |
| − | import numpy as np
| |
| − | import pandas as pd
| |
| − | | |
| − | | |
| − | df = pd.DataFrame({'A':[1,2,np.nan],
| |
| − | 'B':[5,np.nan,np.nan],
| |
| − | 'C':[1,2,3]})
| |
| − | | |
| − | df
| |
| − | # Output:
| |
| − | A B C
| |
| − | 0 1.0 5.0 1
| |
| − | 1 2.0 NaN 2
| |
| − | 2 NaN NaN 3
| |
| − | | |
| − | | |
| − | '''By default, dropna() drop all the rows without Null values:'''
| |
| − | df.dropna()
| |
| − | df.dropna(axis=0) # Same as default
| |
| − | # Output:
| |
| − | A B C
| |
| − | 0 1.0 5.0 1
| |
| − | | |
| − | | |
| − | '''If we want to display all the columns without Null values:'''
| |
| − | df.dropna(axis=1)
| |
| − | | |
| − | | |
| − | '''If we want to display all the rows that have at least 2 non-null values:'''
| |
| − | df.dropna(thresh=2)
| |
| − | # Output:
| |
| − | A B C
| |
| − | 0 1.0 5.0 1
| |
| − | 1 2.0 NaN 2
| |
| − | | |
| − | | |
| − | '''Columns with at least 3 non-null values:'''
| |
| − | df.dropna(thresh=3)
| |
| − | # Output:
| |
| − | A B C
| |
| − | 0 1.0 5.0 1
| |
| − | | |
| − | | |
| − | '''You can also use df.isnull() to check for Null values:
| |
| − | df.isnull()
| |
| − | # Output:
| |
| − | A B C
| |
| − | 0 False False False
| |
| − | 1 False True False
| |
| − | 2 True True False
| |
| − | | |
| − | | |
| − | '''To fill null fields with a given value:'''
| |
| − | df.fillna(value='FILL VALUE')
| |
| − | # Output:
| |
| − | A B C
| |
| − | 0 1 5 1
| |
| − | 1 2 FILL VALUE 2
| |
| − | 2 FILL VALUE FILL VALUE 3
| |
| − | | |
| − | | |
| − | '''But many times what we want to do is to replace these null fields with, for example, the «mean» of the columns. We can do it this way:'''
| |
| − | df['A'].fillna(value=df['A'].mean())
| |
| − | # Output:
| |
| − | 0 1.0
| |
| − | 1 2.0
| |
| − | 2 1.5 # *
| |
| − | Name: A, dtype: float64
| |
| − | | |
| − | '''* The Null field has been filled with the mean of the column'''
| |
| − | </syntaxhighlight>
| |
| − | | |
| − | | |
| − | <br />
| |
| − | | |
| − | ===GroupBy===
| |
| − | The groupby method allows you to group rows of data together and call aggregate functions
| |
| − | | |
| − | Now you can use the .groupby() method to group rows together based on a column name. For instance let's group based off of Company. This will create a DataFrameGroupBy object:
| |
| − | | |
| − | | |
| − | {| class="wikitable" style="width: 100%;"
| |
| − | !
| |
| − | !Method
| |
| − | !Description/Example
| |
| − | |-
| |
| − | !
| |
| − | | colspan="2" |<syntaxhighlight lang="python3">
| |
| − | import pandas as pd
| |
| − | # Create dataframe
| |
| − | data = {'Company':['GOOG','GOOG','MSFT','MSFT','FB','FB'],
| |
| − | 'Person':['Sam','Charlie','Amy','Vanessa','Carl','Sarah'],
| |
| − | 'Sales':[200,120,340,124,243,350]}
| |
| − | | |
| − | df = pd.DataFrame(data)
| |
| − | df
| |
| − | # Output:
| |
| − | Company Person Sales
| |
| − | 0 GOOG Sam 200
| |
| − | 1 GOOG Charlie 120
| |
| − | 2 MSFT Amy 340
| |
| − | 3 MSFT Vanessa 124
| |
| − | 4 FB Carl 243
| |
| − | 5 FB Sarah 350
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | !style="vertical-align:top;"|'''<div style="text-align: left;">GroupBy</div>'''
| |
| − | |'''<code>df.groupby(<nowiki>''</nowiki>)</code>'''
| |
| − | | |
| − | | |
| − | This will create a
| |
| − | DataFrameGroupBy object.
| |
| − | |'''For instance let's group based off of Company:'''<syntaxhighlight lang="python3">
| |
| − | df.groupby('Company')
| |
| − | # Output:
| |
| − | <pandas.core.groupby.generic.DataFrameGroupBy object at 0x7f2027fdd470>
| |
| − | | |
| − | '''You can save this object as a new variable:'''
| |
| − | by_comp = df.groupby("Company")
| |
| − | | |
| − | '''And then call aggregate methods off the object:'''
| |
| − | </syntaxhighlight><br />
| |
| − | |-
| |
| − | ! rowspan="5" style="vertical-align:top;" |<h5 style="text-align:left">We can call aggregate methods on the '''<code>groupBy</code>''' object</h5>
| |
| − | | |
| − | |<code>df.mean()</code>
| |
| − | | |
| − | | |
| − | <code>df.groupby('Company').mean()</code>
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | df.groupby('Company').mean()
| |
| − | by_comp.mean()
| |
| − | # Output:
| |
| − | Sales
| |
| − | Company
| |
| − | FB 296.5
| |
| − | GOOG 160.0
| |
| − | MSFT 232.0
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | |<code>df.std()</code>
| |
| − | | |
| − | | |
| − | <code>df.groupby('Company').std()</code>
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | by_comp.std()
| |
| − | # Output:
| |
| − | Sales
| |
| − | Company
| |
| − | FB 75.660426
| |
| − | GOOG 56.568542
| |
| − | MSFT 152.735065
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | |<code>df.min()</code><code>df.max()</code>
| |
| − | | |
| − | | |
| − | <code>df.groupby('Company').min()</code>
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | by_comp.min()
| |
| − | # Output:
| |
| − | Person Sales
| |
| − | Company
| |
| − | FB Carl 243
| |
| − | GOOG Charlie 120
| |
| − | MSFT Amy 124
| |
| − | | |
| − | by_comp.max()
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | |<code>df.count()</code>
| |
| − | | |
| − | | |
| − | <code>df.groupby('Company').count()</code>
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | by_comp.count()
| |
| − | # Output:
| |
| − | Person Sales
| |
| − | Company
| |
| − | FB 2 2
| |
| − | GOOG 2 2
| |
| − | MSFT 2 2
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | |<code>df.describe()</code>
| |
| − | | |
| − | | |
| − | <code>df.groupby('Company').describe()</code>
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | by_comp.describe()
| |
| − | # Output:
| |
| − | Sales
| |
| − | Company
| |
| − | FB count 2.000000
| |
| − | mean 296.500000
| |
| − | std 75.660426
| |
| − | min 243.000000
| |
| − | 25% 269.750000
| |
| − | 50% 296.500000
| |
| − | 75% 323.250000
| |
| − | max 350.000000
| |
| − | GOOG count 2.000000
| |
| − | mean 160.000000
| |
| − | std 56.568542
| |
| − | min 120.000000
| |
| − | 25% 140.000000
| |
| − | 50% 160.000000
| |
| − | 75% 180.000000
| |
| − | max 200.000000
| |
| − | MSFT count 2.000000
| |
| − | mean 232.000000
| |
| − | std 152.735065
| |
| − | min 124.000000
| |
| − | 25% 178.000000
| |
| − | 50% 232.000000
| |
| − | 75% 286.000000
| |
| − | max 340.000000
| |
| − | | |
| − | | |
| − | by_comp.describe().transpose()
| |
| − | by_comp.describe().transpose()['GOOG']
| |
| − | # Output:
| |
| − | count mean std min 25% 50% 75% max
| |
| − | Sales 2.0 160.0 56.568542 120.0 140.0 160.0 180.0 200.0
| |
| − | </syntaxhighlight>
| |
| − | |}
| |
| − | <br />
| |
| − | | |
| − | ===Concatenation - Merging - Joining===
| |
| − | | |
| − | {| class="wikitable" style="width: 100%;"
| |
| − | |+
| |
| − | !
| |
| − | !Method
| |
| − | !Description/Comments
| |
| − | !Example
| |
| − | |-
| |
| − | !style="vertical-align:top;"|<h4 style="text-align:left">Concatenation</h4>
| |
| − | |'''<code>pd.concat()</code>'''
| |
| − | |<div style="width:180px">
| |
| − | Concatenation basically glues
| |
| − | together DataFrames.
| |
| − | | |
| − | Keep in mind that dimensions should match along the axis you are concatenating on.
| |
| − | | |
| − | You can use <code>pd.concat</code> and pass in a list of DataFrames to concatenate together.
| |
| − | </div>
| |
| − | |Example:
| |
| − | <syntaxhighlight lang="python3">
| |
| − | import pandas as pd
| |
| − | </syntaxhighlight>
| |
| − | <div class="mw-collapsible mw-collapsed" style="">
| |
| − | {| style="border-spacing: 2px; width: 100%;"
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
| |
| − | 'B': ['B0', 'B1', 'B2', 'B3'],
| |
| − | 'C': ['C0', 'C1', 'C2', 'C3'],
| |
| − | 'D': ['D0', 'D1', 'D2', 'D3']},
| |
| − | index=[0, 1, 2, 3])
| |
| − | </syntaxhighlight>
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | df1
| |
| − | # Output:
| |
| − | A B C D
| |
| − | 0 A0 B0 C0 D0
| |
| − | 1 A1 B1 C1 D1
| |
| − | 2 A2 B2 C2 D2
| |
| − | 3 A3 B3 C3 D3
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],
| |
| − | 'B': ['B4', 'B5', 'B6', 'B7'],
| |
| − | 'C': ['C4', 'C5', 'C6', 'C7'],
| |
| − | 'D': ['D4', 'D5', 'D6', 'D7']},
| |
| − | index=[4, 5, 6, 7])
| |
| − | </syntaxhighlight>
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | df2
| |
| − | # Output:
| |
| − | A B C D
| |
| − | 4 A4 B4 C4 D4
| |
| − | 5 A5 B5 C5 D5
| |
| − | 6 A6 B6 C6 D6
| |
| − | 7 A7 B7 C7 D7
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | df3 = pd.DataFrame({'A': ['A8', 'A9', 'A10', 'A11'],
| |
| − | 'B': ['B8', 'B9', 'B10', 'B11'],
| |
| − | 'C': ['C8', 'C9', 'C10', 'C11'],
| |
| − | 'D': ['D8', 'D9', 'D10', 'D11']},
| |
| − | index=[8, 9, 10, 11])
| |
| − | </syntaxhighlight>
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | df3
| |
| − | # Output:
| |
| − | A B C D
| |
| − | 8 A8 B8 C8 D8
| |
| − | 9 A9 B9 C9 D9
| |
| − | 10 A10 B10 C10 D10
| |
| − | 11 A11 B11 C11 D11
| |
| − | </syntaxhighlight>
| |
| − | |}
| |
| − | </div>
| |
| − | | |
| − | | |
| − | <syntaxhighlight lang="python3">
| |
| − | pd.concat([df1,df2,df3])
| |
| − | pd.concat([df1,df2,df3],ignore_index=True)
| |
| − | </syntaxhighlight>
| |
| − | <div class="mw-collapsible mw-collapsed" style="">
| |
| − | <syntaxhighlight lang="python3">
| |
| − | # Output:
| |
| − | A B C D
| |
| − | 0 A0 B0 C0 D0
| |
| − | 1 A1 B1 C1 D1
| |
| − | 2 A2 B2 C2 D2
| |
| − | 3 A3 B3 C3 D3
| |
| − | 4 A4 B4 C4 D4
| |
| − | 5 A5 B5 C5 D5
| |
| − | 6 A6 B6 C6 D6
| |
| − | 7 A7 B7 C7 D7
| |
| − | 8 A8 B8 C8 D8
| |
| − | 9 A9 B9 C9 D9
| |
| − | 10 A10 B10 C10 D10
| |
| − | 11 A11 B11 C11 D11
| |
| − | </syntaxhighlight>
| |
| − | </div>
| |
| − | | |
| − | | |
| − | <syntaxhighlight lang="python3">
| |
| − | pd.concat([df1,df2,df3],axis=1)
| |
| − | </syntaxhighlight>
| |
| − | <div class="mw-collapsible mw-collapsed" style="">
| |
| − | <syntaxhighlight lang="python3">
| |
| − | A B C D A B C D A B C D
| |
| − | 0 A0 B0 C0 D0 NaN NaN NaN NaN NaN NaN NaN NaN
| |
| − | 1 A1 B1 C1 D1 NaN NaN NaN NaN NaN NaN NaN NaN
| |
| − | 2 A2 B2 C2 D2 NaN NaN NaN NaN NaN NaN NaN NaN
| |
| − | 3 A3 B3 C3 D3 NaN NaN NaN NaN NaN NaN NaN NaN
| |
| − | 4 NaN NaN NaN NaN A4 B4 C4 D4 NaN NaN NaN NaN
| |
| − | 5 NaN NaN NaN NaN A5 B5 C5 D5 NaN NaN NaN NaN
| |
| − | 6 NaN NaN NaN NaN A6 B6 C6 D6 NaN NaN NaN NaN
| |
| − | 7 NaN NaN NaN NaN A7 B7 C7 D7 NaN NaN NaN NaN
| |
| − | 8 NaN NaN NaN NaN NaN NaN NaN NaN A8 B8 C8 D8
| |
| − | 9 NaN NaN NaN NaN NaN NaN NaN NaN A9 B9 C9 D9
| |
| − | 10 NaN NaN NaN NaN NaN NaN NaN NaN A10 B10 C10 D10
| |
| − | 11 NaN NaN NaN NaN NaN NaN NaN NaN A11 B11 C11 D11
| |
| − | </syntaxhighlight>
| |
| − | </div>
| |
| − | |-
| |
| − | ! rowspan="2" style="vertical-align:top;" |<h4 style="text-align:left">Merging</h4>
| |
| − | | rowspan="2" |<code>'''pd.merge()'''</code>
| |
| − | | rowspan="2" |<div style="width:180px">
| |
| − | The <code>merge()</code> function allows you to merge DataFrames together using a similar logic as merging SQL Tables together.
| |
| − | </div>
| |
| − | | |
| − | |Example 1:
| |
| − | <syntaxhighlight lang="python3">
| |
| − | import pandas as pd
| |
| − | </syntaxhighlight>
| |
| − | <div class="mw-collapsible mw-collapsed" style="">
| |
| − | {| style="border-spacing: 2px;"
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | left = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
| |
| − | 'A': ['A0', 'A1', 'A2', 'A3'],
| |
| − | 'B': ['B0', 'B1', 'B2', 'B3']})
| |
| − | </syntaxhighlight>
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | left
| |
| − | # Output:
| |
| − | A B key
| |
| − | 0 A0 B0 K0
| |
| − | 1 A1 B1 K1
| |
| − | 2 A2 B2 K2
| |
| − | 3 A3 B3 K3
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | right = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
| |
| − | 'C': ['C0', 'C1', 'C2', 'C3'],
| |
| − | 'D': ['D0', 'D1', 'D2', 'D3']})
| |
| − | </syntaxhighlight>
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | right
| |
| − | # Output:
| |
| − | C D key
| |
| − | 0 C0 D0 K0
| |
| − | 1 C1 D1 K1
| |
| − | 2 C2 D2 K2
| |
| − | 3 C3 D3 K3
| |
| − | </syntaxhighlight>
| |
| − | |}
| |
| − | </div>
| |
| − | | |
| − | | |
| − | <syntaxhighlight lang="python3">
| |
| − | pd.merge(left,right,how='inner',on='key')
| |
| − | </syntaxhighlight>
| |
| − | | |
| − | <div class="mw-collapsible mw-collapsed" style="">
| |
| − | <syntaxhighlight lang="python3">
| |
| − | # Output:
| |
| − | key A B C D
| |
| − | 0 K0 A0 B0 C0 D0
| |
| − | 1 K1 A1 B1 C1 D1
| |
| − | 2 K2 A2 B2 C2 D2
| |
| − | 3 K3 A3 B3 C3 D3
| |
| − | </syntaxhighlight>
| |
| − | </div>
| |
| − | |-
| |
| − | |Example 2:
| |
| − | <syntaxhighlight lang="python3">
| |
| − | import pandas as pd
| |
| − | </syntaxhighlight>
| |
| − | <div class="mw-collapsible mw-collapsed" style="">
| |
| − | | |
| − | {| style="border-spacing: 2px;"
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],
| |
| − | 'key2': ['K0', 'K1', 'K0', 'K1'],
| |
| − | 'A': ['A0', 'A1', 'A2', 'A3'],
| |
| − | 'B': ['B0', 'B1', 'B2', 'B3']})
| |
| − | </syntaxhighlight>
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | left
| |
| − | # Output:
| |
| − | key1 key2 A B
| |
| − | 0 K0 K0 A0 B0
| |
| − | 1 K0 K1 A1 B1
| |
| − | 2 K1 K0 A2 B2
| |
| − | 3 K2 K1 A3 B3
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],
| |
| − | 'key2': ['K0', 'K0', 'K0', 'K0'],
| |
| − | 'C': ['C0', 'C1', 'C2', 'C3'],
| |
| − | 'D': ['D0', 'D1', 'D2', 'D3']})
| |
| − | </syntaxhighlight>
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | right
| |
| − | # Output:
| |
| − | key1 key2 C D
| |
| − | 0 K0 K0 C0 D0
| |
| − | 1 K1 K0 C1 D1
| |
| − | 2 K1 K0 C2 D2
| |
| − | 3 K2 K0 C3 D3
| |
| − | </syntaxhighlight>
| |
| − | |}
| |
| − | </div><br />
| |
| − | | |
| − | | |
| − | <syntaxhighlight lang="python3">
| |
| − | pd.merge(left, right, on=['key1', 'key2'])
| |
| − | </syntaxhighlight>
| |
| − | <div class="mw-collapsible mw-collapsed" style="">
| |
| − | <syntaxhighlight lang="python3">
| |
| − | # Output:
| |
| − | A B key1 key2 C D
| |
| − | 0 A0 B0 K0 K0 C0 D0
| |
| − | 1 A2 B2 K1 K0 C1 D1
| |
| − | 2 A2 B2 K1 K0 C2 D2
| |
| − | </syntaxhighlight>
| |
| − | </div>
| |
| − | | |
| − | | |
| − | <syntaxhighlight lang="python3">
| |
| − | pd.merge(left, right, how='outer', on=['key1', 'key2'])
| |
| − | </syntaxhighlight>
| |
| − | <div class="mw-collapsible mw-collapsed" style="">
| |
| − | <syntaxhighlight lang="python3">
| |
| − | # Output:
| |
| − | A B key1 key2 C D
| |
| − | 0 A0 B0 K0 K0 C0 D0
| |
| − | 1 A1 B1 K0 K1 NaN NaN
| |
| − | 2 A2 B2 K1 K0 C1 D1
| |
| − | 3 A2 B2 K1 K0 C2 D2
| |
| − | 4 A3 B3 K2 K1 NaN NaN
| |
| − | 5 NaN NaN K2 K0 C3 D3
| |
| − | </syntaxhighlight>
| |
| − | </div>
| |
| − | | |
| − | | |
| − | <syntaxhighlight lang="python3">
| |
| − | pd.merge(left, right, how='right', on=['key1', 'key2'])
| |
| − | </syntaxhighlight>
| |
| − | <div class="mw-collapsible mw-collapsed" style="">
| |
| − | <syntaxhighlight lang="python3">
| |
| − | # Output:
| |
| − | A B key1 key2 C D
| |
| − | 0 A0 B0 K0 K0 C0 D0
| |
| − | 1 A2 B2 K1 K0 C1 D1
| |
| − | 2 A2 B2 K1 K0 C2 D2
| |
| − | 3 NaN NaN K2 K0 C3 D3
| |
| − | </syntaxhighlight>
| |
| − | </div>
| |
| − | | |
| − | | |
| − | <syntaxhighlight lang="python3">
| |
| − | pd.merge(left, right, how='left', on=['key1', 'key2'])
| |
| − | </syntaxhighlight>
| |
| − | <div class="mw-collapsible mw-collapsed" style="">
| |
| − | <syntaxhighlight lang="python3">
| |
| − | # Output:
| |
| − | A B key1 key2 C D
| |
| − | 0 A0 B0 K0 K0 C0 D0
| |
| − | 1 A1 B1 K0 K1 NaN NaN
| |
| − | 2 A2 B2 K1 K0 C1 D1
| |
| − | 3 A2 B2 K1 K0 C2 D2
| |
| − | 4 A3 B3 K2 K1 NaN NaN
| |
| − | </syntaxhighlight>
| |
| − | </div>
| |
| − | |-
| |
| − | !style="vertical-align:top;"|<h4 style="text-align:left">Joining</h4>
| |
| − | |'''<code>df.join(df)</code>'''
| |
| − | |<div style="width:180px">
| |
| − | Joining is a convenient method for combining the columns of two potentially differently-indexed DataFrames into a single result DataFrame.
| |
| − | </div>
| |
| − | |Example:<div class="mw-collapsible mw-collapsed" style="">
| |
| − | | |
| − | {| style="border-spacing: 2px;"
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],
| |
| − | 'B': ['B0', 'B1', 'B2']},
| |
| − | index=['K0', 'K1', 'K2'])
| |
| − | </syntaxhighlight>
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | left
| |
| − | # Output:
| |
| − | A B
| |
| − | K0 A0 B0
| |
| − | K1 A1 B1
| |
| − | K2 A2 B2
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | right = pd.DataFrame({'C': ['C0', 'C2', 'C3'],
| |
| − | 'D': ['D0', 'D2', 'D3']},
| |
| − | index=['K0', 'K2', 'K3'])
| |
| − | </syntaxhighlight>
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | right
| |
| − | # Output:
| |
| − | C D
| |
| − | K0 C0 D0
| |
| − | K2 C2 D2
| |
| − | K3 C3 D3
| |
| − | </syntaxhighlight>
| |
| − | |}
| |
| − | </div>
| |
| − | | |
| − | | |
| − | <syntaxhighlight lang="python3">
| |
| − | left.join(right)
| |
| − | </syntaxhighlight>
| |
| − | <div class="mw-collapsible mw-collapsed" style="">
| |
| − | <syntaxhighlight lang="python3">
| |
| − | # Output:
| |
| − | A B C D
| |
| − | K0 A0 B0 C0 D0
| |
| − | K1 A1 B1 NaN NaN
| |
| − | K2 A2 B2 C2 D2
| |
| − | </syntaxhighlight>
| |
| − | </div>
| |
| − | | |
| − | | |
| − | <syntaxhighlight lang="python3">
| |
| − | left.join(right, how='outer')
| |
| − | </syntaxhighlight>
| |
| − | <div class="mw-collapsible mw-collapsed" style="">
| |
| − | <syntaxhighlight lang="python3">
| |
| − | # Output:
| |
| − | A B C D
| |
| − | K0 A0 B0 C0 D0
| |
| − | K1 A1 B1 NaN NaN
| |
| − | K2 A2 B2 C2 D2
| |
| − | K3 NaN NaN C3 D3
| |
| − | </syntaxhighlight>
| |
| − | </div>
| |
| − | |}
| |
| − | | |
| − | | |
| − | <br />
| |
| − | | |
| − | ===Comparison with SQL===
| |
| − | * https://pandas.pydata.org/pandas-docs/stable/getting_started/comparison/comparison_with_sql.html
| |
| − | * https://www.edureka.co/blog/sql-joins-types
| |
| − | | |
| − | | |
| − | <br />
| |
| − | ===Some operations===
| |
| − | {| class="wikitable"
| |
| − | !
| |
| − | ! colspan="2" |Method/Operator
| |
| − | !Description/Comments
| |
| − | !Example
| |
| − | |-
| |
| − | !
| |
| − | | colspan="2" |
| |
| − | | colspan="2" |<syntaxhighlight lang="python3">
| |
| − | import pandas as pd
| |
| − | df = pd.DataFrame({'col1':[1,2,3,4],'col2':[444,555,666,444],'col3':['abc','def','ghi','xyz']})
| |
| − | df.head()
| |
| − | # Output:
| |
| − | col1 col2 col3
| |
| − | 0 1 444 abc
| |
| − | 1 2 555 def
| |
| − | 2 3 666 ghi
| |
| − | 3 4 444 xyz
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | !style="vertical-align:top;"|<h4 style="text-align:left">Selecting Data</h4>
| |
| − | | colspan="2" |
| |
| − | |Select from DataFrame using criteria from multiple columns.
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | newdf = df[(df['col1']>2) & (df['col2']==444)]
| |
| − | newdf
| |
| − | # Output:
| |
| − | col1 col2 col3
| |
| − | 3 4 444 xyz
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | !style="vertical-align:top;"|<h4 style="text-align:left">Info on Unique Values</h4>
| |
| − | | colspan="2" |'''<code>df.unique()</code>'''
| |
| − | |
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | df['col2'].unique()
| |
| − | # Output:
| |
| − | array([444, 555, 666])
| |
| − | | |
| − | df['col2'].nunique()
| |
| − | # Output:
| |
| − | 3
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | !style="vertical-align:top;"|<h4 style="text-align:left">Count values</h4>
| |
| − | | colspan="2" |'''<code>df.value_counts()</code>'''
| |
| − | |
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | df['col2'].value_counts()
| |
| − | # Output:
| |
| − | 444 2
| |
| − | 555 1
| |
| − | 666 1
| |
| − | Name: col2, dtype: int64
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | !style="vertical-align:top;"|<h4 style="text-align:left">Removing a Column</h4>
| |
| − | | colspan="2" |'''<code>del df['col']</code>'''
| |
| − | |
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | del df['col1']
| |
| − | # Output:
| |
| − | col2 col3
| |
| − | 0 444 abc
| |
| − | 1 555 def
| |
| − | 2 666 ghi
| |
| − | 3 444 xyz
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | !style="vertical-align:top;"|<h4 style="text-align:left">Get column and index names</h4>
| |
| − | | colspan="2" |'''<code>df.columns</code>'''
| |
| − | '''<code>df.index</code>'''
| |
| − | |
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | df.columns
| |
| − | # Output:
| |
| − | Index(['col2', 'col3'], dtype='object')
| |
| − | | |
| − | df.index
| |
| − | # Output:
| |
| − | RangeIndex(start=0, stop=4, step=1)
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | !style="vertical-align:top;"|<h4 style="text-align:left">Sorting and Ordering a DataFrame</h4>
| |
| − | | colspan="2" |'''<code>df.sort_values()</code>'''
| |
| − | |
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | df.sort_values(by='col2') #inplace=False by default
| |
| − | # Output:
| |
| − | col2 col3
| |
| − | 0 444 abc
| |
| − | 3 444 xyz
| |
| − | 1 555 def
| |
| − | 2 666 ghi
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | ! rowspan="3" style="vertical-align:top;" |<h4 style="text-align:left">Applying Functions</h4>
| |
| − | | rowspan="2" |<syntaxhighlight lang="python3">
| |
| − | df[''].apply(some_function)
| |
| − | </syntaxhighlight>
| |
| − | |<code>def function():</code>
| |
| − | |We can define our own function
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | def times2(x):
| |
| − | return x*2
| |
| − | | |
| − | df['col1'].apply(times2)
| |
| − | # Output
| |
| − | 0 2
| |
| − | 1 4
| |
| − | 2 6
| |
| − | 3 8
| |
| − | Name: col1, dtype: int64
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | |<code>len</code>
| |
| − | |
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | df['col3'].apply(len)
| |
| − | # Output:
| |
| − | 0 3
| |
| − | 1 3
| |
| − | 2 3
| |
| − | 3 3
| |
| − | Name: col3, dtype: int64
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | | colspan="2" |'''<code>df[<nowiki>''</nowiki>].sum()</code>'''
| |
| − | |Sum values in a column
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | df['col1'].sum()
| |
| − | # Output:
| |
| − | 10
| |
| − | </syntaxhighlight>
| |
| − | |-
| |
| − | !
| |
| − | | colspan="2" |'''<code>df.pivot_table()</code>'''
| |
| − | |
| |
| − | |<syntaxhighlight lang="python3">
| |
| − | data = {'A':['foo','foo','foo','bar','bar','bar'],
| |
| − | 'B':['one','one','two','two','one','one'],
| |
| − | 'C':['x','y','x','y','x','y'],
| |
| − | 'D':[1,3,2,5,4,1]}
| |
| − | | |
| − | df = pd.DataFrame(data)
| |
| − | df
| |
| − | # Output:
| |
| − | A B C D
| |
| − | 0 foo one x 1
| |
| − | 1 foo one y 3
| |
| − | 2 foo two x 2
| |
| − | 3 bar two y 5
| |
| − | 4 bar one x 4
| |
| − | 5 bar one y 1
| |
| − | | |
| − | df.pivot_table(values='D',index=['A', 'B'],columns=['C'])
| |
| − | # Output:
| |
| − | C x y
| |
| − | A B
| |
| − | bar one 4.0 1.0
| |
| − | two NaN 5.0
| |
| − | foo one 1.0 3.0
| |
| − | two 2.0 NaN
| |
| − | </syntaxhighlight>
| |
| − | |}
| |
| − | | |
| − | | |
| − | <br />
| |
