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Abstract

number">2</span>,<span class="hljs-number">3</span>]</pre></div><div id="fc42"><pre><span class="hljs-function"><span class="hljs-title">list</span><span class="hljs-params">(combinations(lst,<span class="hljs-number">2</span>)</span></span>)</pre></div><div id="ab6a"><pre><span class="hljs-attr"># [[(1</span>,<span class="hljs-number">2</span>,), <span class="hljs-comment">(1,3)</span>, <span class="hljs-comment">(2,3)</span>]</pre></div><div id="63ce"><pre><span class="hljs-function"><span class="hljs-title">list</span><span class="hljs-params">(combinations_with_replacement(lst,<span class="hljs-number">2</span>)</span></span>)</pre></div><div id="ef1e"><pre><span class="hljs-attr"># [[(1</span>,<span class="hljs-number">1</span>),<span class="hljs-comment">(1,2)</span>,<span class="hljs-comment">(1,3)</span>,<span class="hljs-comment">(2,2)</span>,<span class="hljs-comment">(2,3)</span>,<span class="hljs-comment">(3,3)</span>]</pre></div><div id="256b"><pre><span class="hljs-function"><span class="hljs-title">list</span><span class="hljs-params">(combinations(lst,<span class="hljs-number">3</span>)</span></span>)</pre></div><div id="49f7"><pre><span class="hljs-meta">#[(1,2,3)]</span></pre></div><h2 id="12fc">9. Thousands Separator</h2><div id="b950"><pre><span class="hljs-attribute">N</span> <span class="hljs-operator">=</span> <span class="hljs-number">5384200</span></pre></div><div id="c942"><pre><span class="hljs-attr">comma_separated_N</span> = f‘{N: , <span class="hljs-number">1</span>}’</pre></div><div id="72c7"><pre>comm<span class="hljs-built_in">a_separated</span>_N</pre></div><div id="5dd7"><pre><span class="hljs-meta">#5,384,200</span></pre></div><h2 id="523b">10. Sum or Multiply all elements of a list</h2><div id="cf4a"><pre><span class="hljs-keyword">from</span> numpy <span class="hljs-keyword">import</span> prod</pre></div><div id="e739"><pre><span class="hljs-keyword">from</span> functools <span class="hljs-keyword">import</span> reduce</pre></div><div id="c8ac"><pre><span class="hljs-attr">lst</span> = [<span class="hljs-number">1</span>,<span class="hljs-number">2</span>,<span class="hljs-number">3</span>,<span class="hljs-number">4</span>]</pre></div><div id="e540"><pre><span class="hljs-attr">p1</span> = prod(lst) <span class="hljs-comment">#24</span></pre></div><div id="1d7a"><pre><span class="hljs-attr">p2</span> = reduce((lambda x, y: x * y), lst) <span class="hljs-comment">#24</span></pre></div><div id="0ef1"><pre><span class="hljs-attr">s</span> = sum(lst) <span class="hljs-comment">#10</span></pre></div><h2 id="ae20">11. Get quotient and remainder</h2><div id="53e1"><pre><span class="hljs-attribute">q</span>, r= divmod(<span class="hljs-number">25</span>,<span class="hljs-number">6</span>)</pre></div><div id="54ff"><pre><span class="hljs-meta"># q, r= 4, 1</span></pre></div><h2 id="46c7">12. Most frequent element of a list</h2><div id="9901"><pre>lst = [<span class="hljs-number">1,2,3,1</span>,<span class="hljs-number">1,1,10,20</span>,<span class="hljs-number">3,4,5,6</span>]</pre></div><div id="0460"><pre>most_freq_e = <span class="hljs-built_in">Counter</span>(lst)<span class="hljs-selector-class">.most_common</span>()<span class="hljs-selector-attr">[0]</span><span class="hljs-selector-attr">[0]</span> </pre></div><div id="6707"><pre><span class="hljs-meta">#1</span></pre></div><h2 id="ed25">13. Sort Dictionary based on multiple conditions</h2><div id="d627"><pre><span class="hljs-attribute">d</span> = {“HA”:<span class="hljs-number">5</span>, “AN”:<span class="hljs-number">5</span>, “AK”:<span class="hljs-number">10</span>, “STATO”:<span class="hljs-number">0</span> , “HEW”:<span class="hljs-number">10</span>}</pre></div><div id="9b2a"><pre>lst = <span class="hljs-built_in">sorted</span>(d<span class="hljs-selector-class">.items</span>( ), key = lamda x: (x<span class="hljs-selector-attr">[1]</span>, <span class="hljs-built_in">len</span>(x<span class="hljs-selector-attr">[0]</span>), x<span class="hljs-selector-attr">[0]</span>)) </pre></div><div id="5194"><pre>#### sorts based on value first, then length <span class="hljs-keyword">

Options

of</span> key and finally on the key itself (so alphabetically <span class="hljs-keyword">in</span> this <span class="hljs-keyword">case</span>) #####</pre></div><div id="5b1c"><pre><span class="hljs-keyword">new</span><span class="hljs-type">_d</span> = {k:<span class="hljs-type">v for k</span>,v <span class="hljs-keyword">in</span> lst}</pre></div><div id="24cc"><pre><span class="hljs-keyword">new</span><span class="hljs-type">_d</span> </pre></div><div id="9a7f"><pre><span class="hljs-meta"># {“STATO”:0, “AN”:5, “HA”:5, “AK”:10, “HEW”:10}</span></pre></div><h2 id="ab3b">14. Transpose a matrix</h2><p id="1746">mtrx= [[1,2,3], [3,4,5], [5,6,7]]</p><p id="0e25">lst = list(list(x) for x in zip(*mtrx))</p><p id="f901"># lst = [[1,3,5], [2,4,6], [3,6,7]]</p><h2 id="40b2">15. Use Switch Statement for Python 3.10</h2><p id="6b90">Refer to <a href="https://docs.python.org/3.10/whatsnew/3.10.html">https://docs.python.org/3.10/whatsnew/3.10.html</a></p><div id="db1f"><pre>def get_mood(<span class="hljs-keyword">day</span>): <span class="hljs-keyword">match</span> <span class="hljs-keyword">day</span>: <span class="hljs-keyword">case</span> ‘Monday’: <span class="hljs-keyword">return</span> ‘Oh…’ <span class="hljs-keyword">case</span> ‘Thursday’: <span class="hljs-keyword">return</span> ‘Getting <span class="hljs-keyword">close</span><span class="hljs-operator">!</span>’ <span class="hljs-keyword">case</span> ‘Friday’: <span class="hljs-keyword">return</span> ‘Almost there<span class="hljs-operator">!</span>’ <span class="hljs-keyword">case</span> ‘Saturday’ <span class="hljs-operator">|</span> ‘Sunday’: <span class="hljs-keyword">return</span> ‘Weekend<span class="hljs-operator">!</span><span class="hljs-operator">!</span><span class="hljs-operator">!</span>’ <span class="hljs-keyword">case</span> _: <span class="hljs-keyword">return</span> ‘Meh…’</pre></div><p id="6e1b">Good <a href="https://towardsdatascience.com/switch-case-statements-are-coming-to-python-d0caf7b2bfd3">tutorial</a> on this.</p><h2 id="f2a5">16. Loading multiple pandas dataframes onto each element of list</h2><div id="586b"><pre><span class="hljs-keyword">import</span> glob <span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd</pre></div><div id="2a2e"><pre><span class="hljs-keyword">files</span>= <span class="hljs-built_in">glob</span>.<span class="hljs-built_in">glob</span>({path}) df_list= [pd.read_csv(<span class="hljs-keyword">file</span>) <span class="hljs-keyword">for</span> <span class="hljs-keyword">file</span> in <span class="hljs-keyword">files</span>]</pre></div><h2 id="866d">18. Insert Image in R markdown or Jupyter Notebook</h2><div id="0d71"><pre># <span class="hljs-keyword">Method</span> 1</pre></div><div id="a62d"><pre>! [Alt <span class="hljs-built_in">text</span>] (<span class="hljs-regexp">/Users/</span>Antonio/Documents/images/tufte.book.jpg)</pre></div><div id="18d4"><pre># <span class="hljs-keyword">Method</span> 2</pre></div><div id="997d"><pre>```{r pressure, <span class="hljs-attribute">echo</span>=<span class="hljs-literal">FALSE</span>, fig.<span class="hljs-attribute">cap</span>=”A caption”, <span class="hljs-attribute">message</span>=<span class="hljs-literal">FALSE</span>, <span class="hljs-attribute">warning</span>=<span class="hljs-literal">FALSE</span>, out.<span class="hljs-attribute">width</span>=, paged.<span class="hljs-attribute">print</span>=<span class="hljs-literal">FALSE</span>} knitr::include_graphics(“C:/Users/josh/Desktop/nn.jpg”)

16 Python and Pandas Hacks that will save you time in your project

Make your Data Science workflow more efficient

1. Check memory usage of your objects

import sys

lst = range(0, 500)

print(sys.getsizeof(lst))

2. Converting string to byte

s = “I want to convert this string to byte”

s.encode( )

# b’I want to convert this string to byte’

3. Merge two list objects

a = [1,2,3]
b = [10,20,30]

a.extend(b)
# [1,2,3,10,20,30]

4. Retrieve longest and shortest string in a list

word_list = [“I”, “am”, “handsome”]

max(word_list, key=len)

# ‘handsome’

min(word_list,key=len)

# ‘I’

5. Count the number of occurrences of a character in a string

"please find the occurrence of a in this sentence'.count('a')
# 2

6. Unpack unequal number of values into variables

x, y, z* = ['a','b','c','d','e','f','g']

x 
# 'a'

y
# 'b'

z
# 'c','d','e','f','g'

7. Flatten a list

lst = [[1,2], [3,4], [5,6]]

flattened_lst = [i for j in list for i in j ]

lst = [1,2,3,4,5,6]

8. Print all combinations in a list

from itertools import combinations, combinations_with_replacement

lst= [1,2,3]

list(combinations(lst,2))

# [[(1,2,), (1,3), (2,3)]

list(combinations_with_replacement(lst,2))

# [[(1,1),(1,2),(1,3),(2,2),(2,3),(3,3)]

list(combinations(lst,3))

#[(1,2,3)]

9. Thousands Separator

N = 5384200

comma_separated_N = f‘{N: , 1}’

comma_separated_N

#5,384,200

10. Sum or Multiply all elements of a list

from numpy import prod

from functools import reduce

lst = [1,2,3,4]

p1 = prod(lst) #24

p2 = reduce((lambda x, y: x * y), lst) #24

s = sum(lst) #10

11. Get quotient and remainder

q, r= divmod(25,6)

# q, r= 4, 1

12. Most frequent element of a list

lst = [1,2,3,1,1,1,10,20,3,4,5,6]

most_freq_e = Counter(lst).most_common()[0][0]

#1

13. Sort Dictionary based on multiple conditions

d = {“HA”:5, “AN”:5, “AK”:10, “STATO”:0 , “HEW”:10}

lst = sorted(d.items( ), key = lamda x: (x[1], len(x[0]), x[0]))

#### sorts based on value first, then length of key and finally on the key itself (so alphabetically in this case) #####

new_d = {k:v for k,v in lst}

new_d

# {“STATO”:0, “AN”:5, “HA”:5, “AK”:10, “HEW”:10}

14. Transpose a matrix

mtrx= [[1,2,3], [3,4,5], [5,6,7]]

lst = list(list(x) for x in zip(*mtrx))

# lst = [[1,3,5], [2,4,6], [3,6,7]]

15. Use Switch Statement for Python 3.10

Refer to https://docs.python.org/3.10/whatsnew/3.10.html

def get_mood(day):
    match day:
        case ‘Monday’:
            return ‘Oh…’
        case ‘Thursday’:
            return ‘Getting close!’
        case ‘Friday’:
            return ‘Almost there!’
        case ‘Saturday’ | ‘Sunday’:
            return ‘Weekend!!!’
        case _:
            return ‘Meh…’

Good tutorial on this.

16. Loading multiple pandas dataframes onto each element of list

import glob
import pandas as pd

files= glob.glob({path})
df_list= [pd.read_csv(file) for file in files]

18. Insert Image in R markdown or Jupyter Notebook

# Method 1

! [Alt text] (/Users/Antonio/Documents/images/tufte.book.jpg)

# Method 2

```{r pressure, echo=FALSE, fig.cap=”A caption”, message=FALSE, warning=FALSE, out.width=, paged.print=FALSE}
knitr::include_graphics(“C:/Users/josh/Desktop/nn.jpg”)
```

About the Author

Data Scientist. Working as a research associate at the Criminal Justice Administrative Records System (CJARS) economics lab at the University of Michigan. Incoming PhD student in Informatics. He loves sports, working-out, cooking good Asian food, watching kdramas and making / performing music and most importantly worshiping Jesus Christ. Checkout his website!