python - How to check if a column exists in Pandas - Stack Overflow

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Mon Nov 16 2020 13:04:00 GMT+0000 (UTC)

if 'A' in df.columns:

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https://stackoverflow.com/questions/24870306/how-to-check-if-a-column-exists-in-pandas

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@haviernick - Mon Mar 15 2021 07:15:15 GMT+0000 (UTC)

Check for multiple columns exists in DataFrame if set(['Col1','Col2']).issubset(df.columns): print("Yes") else: print("No") http://net-informations.com/ds/pd/exists.htm

Pandas

@arielvol

#python

Getting the index of an item in a list containing it in Python

>>> ["foo", "bar", "baz"].index("bar")
1

#python #dates #functions #python3.8

How to parse a String into Datetime in Python

from datetime import datetime

datetime_object = datetime.strptime('Jun 1 2005  1:33PM', '%b %d %Y %I:%M%p')

#javascript #python #search #historicalcode #google #algorithms

Google’s PageRank Algorithm from 1996 - the origin of internet search

import numpy as np

def pagerank(M, num_iterations=100, d=0.85):
    N = M.shape[1]
    v = np.random.rand(N, 1)
    v = v / np.linalg.norm(v, 1)
    iteration = 0
    while iteration < num_iterations:
        iteration += 1
        v = d * np.matmul(M, v) + (1 - d) / N
    return v

#python

Could not build wheels for tokenizers which use PEP 517 and cannot be installed directly

https://github.com/pydata/bottleneck/issues/281

#python #python #lists #dictionary

Convert two lists into a dictionary

keys, values)) # {'a': 2, 'c': 4, 'b': 3}
 
 
#make a function: def is the keyword for the function:
def to_dictionary(keys, values):
 
 
#return is the keyword that tells program that function has to return value   
return dict(zip(keys, values))
 
  
 
# keys and values are the lists:
 
keys = ["a", "b", "c"]   
 
values = [2, 3, 4]

#python #python #loops #whileloop

Print the name of 7 days in a week - by using while loop

days = 0
week = [‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’, 3.‘Sunday’]
while day < 7:
print(“Today is” + week[days])
days += 1

#python #multiprocessing

Sync/copy data from source to destination using rsync and multiprocessing

#!/usr/bin/env python
import subprocess
from multiprocessing import Pool
import os

src = "/home/student-03-474f458f89e0/data/prod"
dest = "/home/student-03-474f458f89e0/data/prod_backup"

def run_sync(file):
    print(os.path.join(dest,file))
    subprocess.call(["rsync", "-arq", os.path.join(src,file), os.path.join(dest,file)])

if __name__ == "__main__":
    files = os.listdir(src)
    p = Pool(len(files))
    p.map(run_sync, files)

#python

Raising errors in Python

my_list = [27, 5, 9, 6, 8]

def RemoveValue(myVal):
    if myVal not in my_list:
        raise ValueError("Value must be in the given list")
    else:
        my_list.remove(myVal)
    return my_list

print(RemoveValue(27))
print(RemoveValue(27))

#python

Python Loop through Excel sheets, place into one df - Stack Overflow

import pandas as pd

sheets_dict = pd.read_excel('Book1.xlsx', sheetname=None)

full_table = pd.DataFrame()
for name, sheet in sheets_dict.items():
    sheet['sheet'] = name
    sheet = sheet.rename(columns=lambda x: x.split('\n')[-1])
    full_table = full_table.append(sheet)

full_table.reset_index(inplace=True, drop=True)

print full_table

#python #virtual_environment #virtalenv

Virtual enviornment

virtualenv env

# linux
source env/bin/activate

#windows
env\Scripts\activate.bat

deactivate

#python

python - Creating dataframe from a dictionary where entries have different lengths - Stack Overflow

>>> mydict = {'one': [1,2,3], 2: [4,5,6,7], 3: 8}

>>> dict_df = pd.DataFrame({ key:pd.Series(value) for key, value in mydict.items() })

>>> dict_df

   one  2    3
0  1.0  4  8.0
1  2.0  5  NaN
2  3.0  6  NaN
3  NaN  7  NaN

#python

Convert string to Enum in Python - Stack Overflow

>>> from enum import Enum
>>> class Build(Enum):
...   debug = 200
...   build = 400
... 

Build['debug']

#python #pandas #data-cleaning

Forward fills Column names

def ffill_cols(df, cols_to_fill_name='Unn'):
    """
    Forward fills column names. Propagate last valid column name forward to next invalid column. Works similarly to pandas
    ffill().
    
    :param df: pandas Dataframe; Dataframe
    :param cols_to_fill_name: str; The name of the columns you would like forward filled. Default is 'Unn' as
    the default name pandas gives unnamed columns is 'Unnamed'
    
    :returns: list; List of new column names
    """
    cols = df.columns.to_list()
    for i, j in enumerate(cols):
        if j.startswith(cols_to_fill_name):
            cols[i] = cols[i-1]
    return cols

#python #python #dictionary #mergedictionary #dict

Merge two dictionaries

 
def merge_two_dicts(a, b):
 
 
   c = a.copy()   # make a copy of a
 
   c.update(b)    # modify keys and values of a with the ones from b
 
   return c
 
 
 
 
 
a = { 'x': 1, 'y': 2}
 
b = { 'y': 3, 'z': 4}
 
 
print(merge_two_dicts(a, b)) # {'y': 3, 'x': 1, 'z': 4}

#python #python #math #list

Returns the maximum value of a list in Python

def max_by(lst, fn):
  return max(map(fn, lst))

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