Python server to run html with Node.js script

to run Python server in cmd window,

    python -m http.server 8000

in web browser,

    http://localhost:8000/index.html

If it does not work, 

(1) install Node.js

(2) in cmd window:

    npm create vite@latest  temp  -- --template react

(3) in temp folder in cmd window:

    npm install

    npm run dev

(4) replace the generated src/App.jsx with your file contents and import your CSS in the component or main.jsx

(5) in cmd window, build:

    npm run build

(6) copy all files in the "dist" folder, and now the Python server will work

install and use Jupyter notebook for Python

In cmd window,

To install,

        python -m pip install notebook

To use,

        jupyter notebook

make a Python code executable (.exe)

Method 1.

python -m pip install pyinstaller

python -m PyInstaller --noconfirm --onefile --windowed  code.py

Method 2.

python -m pip install auto-py-to-exe

python -m auto_py_to_exe

adjust column lengths when using Pandas ExcelWriter

 To dynamically adjust all the column lengths,

        writer = pd.ExcelWriter('temp.xlsx') 

        df.to_excel(writer, sheet_name='sheetName', index=False)

        for column in df:

                column_length = max(df[column].astype(str).map(len).max(), len(column))

                col_idx = df.columns.get_loc(column)

                writer.sheets['sheetName'].set_column(col_idx, col_idx, column_length)

        writer.close()

To manually adjust a column using column name,

        col_idx = df.columns.get_loc('columnName')

        writer.sheets['sheetName'].set_column(col_idx, col_idx, 20)

To manually adjust a column using column index,

        writer.sheets['sheetName'].set_column(col_idx, col_idx, 20)

Potential error messages:

        AttributeError: 'Worksheet' object has no attribute 'set_column'

-->  install "xlsxwriter" module

-->  use the installed as the engine,  writer = pd.ExcelWriter('temp.xlsx', engine='xlsxwriter') 

remove "FutureWarning" messages in Python programs

We can remove "FutureWarning" messages in Python programs by adding:

        import warnings 

        warnings.simplefilter(action='ignore', category=FutureWarning)

[Python] join two dataframe according to specific column

 To join two dataframe (df1, df2) according to specific column (e.g., X,Y,Z),

# merge two dataframe comparing X column data of df1 and Y column data of df2

# merge rows where X value on df1 is same to Y value of df2  (other rows will be abandoned)

# in the merged dataframe, "_x" will be added to column label of df1 and "_y" will be added to column label of df2

          pd.merge(df1, df2, left_on="X", right_on="Y")     

# comparing multiple columns

# rows with same X, Y, Z values on df1 and df2 will merge  (other rows will be abandoned)

# in the merged dataframe, "_x" will be added to column label of df1 and "_y" will be added to column label of df2 

          pd.merge(df1, df2, left_on=['X ','Y','Z'], right_on=['X ','Y','Z'])

[Python] to compare/calculate Pandas columns

to compare Pandas columns, 

    print(df['Column1'].equals(df['Column2'])) 

if df['Column1'] is equal to df['Column2'], it will return "True" otherwise "False" 

to calculate/assign values,

    print(df['Column1'] + df['Column2']) 

import numpy as np
import pandas as pd

a = [['10', '1.2', '4.2'], ['15', '70', '0.03'], ['8', '5', '0']]
df = pd.DataFrame(a, columns=['one', 'two', 'three'])

df['que'] = np.where((df['one'] >= df['two']) & (df['one'] <= df['three']), df['one'], np.nan)

result will look like:

          one  two three  que

   0     10    1.2   4.2    10

   1      5     70    0.03  NaN

   2      8     5      0       NaN

[Python] how to find the first N largest/smallest values

import pandas as pd

temp= pd.read_csv("temp.txt", delimiter="\t", skipinitialspace=True, encoding= "utf-8")
print(temp["score"].nlargest(5))    # print top 5 values
print(temp["score"].smallest(5))    # print bottom 5 values

print(temp.loc[((temp['score'] >80) & (temp['score'] <=90))  |  ((temp['math'] >=80) & (temp['math'] <90))].nlargest(5))    # print conditioned top 5 values  

[Python] remove all annotations starting with #

import re
input= open('input.py', 'r', encoding='utf-8').read()

# convert a line (\n) starting with ## into an empty line (\n)  
output= re.sub(re.compile(r"##.*[\n]" ) , "\n" , input)    
open('output.py', 'w', encoding='utf-8').write(output)

[Python] statistics with Pandas DataFrame

import pandas as pd 

ng_list = [{"name": "Jake", "math": 61, "chemistry": 80}, {"name": "Annie", "math": 78, "chemistry": 90}, {"name": "Jane", "math": 71, "chemistry": 54}, {"name": "Sam", "math": 75, "chemistry": 74}, {"name": "Ben", "math": 46, "chemistry": 64}, {"name": "Sky", "math": 38, "chemistry": 77}]  

# create data frame 
df = pd.DataFrame(ng_list) 
print(df, "\n") 

# sum for each row
df["total"] = df["math"] + df["chemistry"]
print(df, "\n") 

# sum for each column
print(df["math"].sum(), "\n") 

# mean with condition
mean_math_bet60_80 = df.loc[(df["math"] >= 60) & (df["math"] <= 80), "math"].mean()
print(mean_math_bet60_80, "\n")

# common statistics for numerical values
print(df.describe(), "\n")

# common statistics for non-numerical values
print(df.describe(include='object'), "\n")


# functions for other staticstics:
# max, min: maximum, mimimum values 
# count: count
# sem: standard error
# mode: most frequently appeared value
# quantile: quantile, e.g., 10% quantile --> df['math'].quantile(0.1)
# corr: corelation between two columes, e.g., df['math'].corr(df['chemistry'])

[Python] concatenate two data frame, and deal with NaN

import pandas as pd 

ng_list1 = [{"name": "Jake", "grade": 80}, {"name": "Annie", "grade": 90}, {"name": "Jane"}] 
ng_list2 = [{"name": "Sam", "grade": 74}, {"name": "Ben", "grade": 64}, {"name": "Sky", "grade": 77}, {"name": "Annie", "grade": 90}] 
 
# create data frame 
df1 = pd.DataFrame(ng_list1)
df2 = pd.DataFrame(ng_list2)
print(df1, "\n")
print(df2, "\n")

# concatenate df1 and df2
df = pd.concat([df1, df2], ignore_index=True)
print(df, "\n") 

# remove duplicates
df.drop_duplicates(inplace=True, ignore_index=True) 

# dealing with NaN
# if you wanna drop any row including NaN, use df.dropna(inplace=True)
# if you wanna drop a row with NaN for all columns, use df.dropna(how="all", inplace=True)
# if you wanna fill all NaN with 0, use df.fillna(0, inplace=True)
df.fillna(0, inplace=True)
print(df, "\n") 

[Python] sort data frame and reset index

import pandas as pd
import numpy as np


n = ["Jake", "Annie", "Charles", "Sam", "David"]
g = [80, 90, 75, 64, 78]

# create a data frame 
df = pd.DataFrame()
df["name"], df["grade"] = n, g 
print(df, "\n")

# sort by name
df.sort_values(by=["name"], ascending=[True], inplace=True)  
print(df, "\n")

# reset index
df.reset_index(inplace=True, drop=True)
print(df, "\n")

[Python] create a new Pandas DataFrame, and add a new column to the data frame

import pandas as pd
import numpy as np


n = ["Annie", "Charles", "David", "Sam"]
g = [80, 90, 75, 78]

# create a data frame using one of lists
df = pd.DataFrame(n, columns=["name"])
print(df, "\n")

# add another list to the data frame
df.insert(len(df.columns), "grade", g)
print(df, "\n")

# dertermine whether pass or not and create a new column for it
df["pass"] = np.where(df["grade"] >=80, "yes", "no")
print(df, "\n")

[Python] how to create a Python code from your GUI design created in Qt Designer

You've finished designing your GUI in Qt Designer and now you want to create a Python code from it.

1. Open "Command Prompt" in Windows.

2. Move to a folder where your GUI has been saved.

3. Run:

          pyuic6   file_name_of_your_GUI.ui   >  file_name_of_a_Python_code_to_be_created.py

Note that pyuic6 is for PyQt6.  If you use PyQt5, it should be pyuic5.

You can skip Step 2 and then file name must include PATH (e.g., c:\user\my_GUI.ui)

[Python] to reset index for a sorted list

import pandas as pd
import numpy as np


data1 = [1, 3, 2, 4]
data2 = [1015, 1014, 1014, 1019]
data3 = [33, 39, 91, 14]

data = [data1, data2, data3]
print('data: ', data, "\n")

data_transposed = np.transpose(data)
print('transposed data: \n', data_transposed, "\n")

df = pd.DataFrame(data_transposed, columns=['quantity', 'date', 'price'])
print('DataFrame: \n', df, "\n")

size = len(df['date'])
evaluation = [df['quantity']*df['price']] 
temp = np.array(evaluation).reshape(size,1) 
df = np.hstack([df, temp]) 
df = pd.DataFrame(df, columns=['quantity', 'date', 'price', 'evaluation'])
print("DataFrame with added column: \n", df, "\n")

df = df.sort_values(['date', 'evaluation'], ascending=[True, True])
df = df[["date", "evaluation", "price", "quantity"]]
print('sorted DataFrame: \n', df, "\n")

df = df.reset_index(drop=True)
print('re-indexed data: \n', df)

[Python] to append/insert data into a list/array

import pandas as pd

data = [[-4, -3, -2, -1], [1, 2, 3, 4]]
data2 = [5, 6, 7, 8]
data3 = [9, 10, 11, 12]

data_list = []
data_list.append(data)
print(data_list)

data_list.append(data2)
print(data_list)

data_list.insert(0, data3)
print(data_list)
print()

# add header (title row) using pandas
df = pd.DataFrame(data, columns=['A', 'B', 'C', 'D'])
print(df, "\n")

temp_df = pd.DataFrame([data2], columns=['A', 'B', 'C', 'D'])
df = pd.concat([df, temp_df]).reset_index(drop=True)
print(df, "\n")

temp_df = pd.DataFrame({"A": data3[0], "B": data3[1], 'C': data3[2], 'D': data3[3]}, index=[0])
df = pd.concat([temp_df, df]).reset_index(drop=True)
print(df, "\n")

# add a new column using pandas
size = len(df)
data2 = [i * 2 for i in range(size)]
temp = pd.Series(data2, name='XYZ')
df = df.join(temp)
print(df)

    

[Python] to sort a list with multiple data types

my_list = [0, "g", "a", "b", "f", 'c', 3, 2, "z"]
print("original list:", my_list)

# sort - method 1
my_list_int = sorted([i for i in my_list if type(i) is int])
my_list_str = sorted([i for i in my_list if type(i) is str])
print("sorted list:", my_list_int + my_list_str)

# sort - method 2
my_list_int = sorted([x for x in my_list if isinstance(x, int)])
my_list_str = sorted([x for x in my_list if isinstance(x, str)])
print("int:", my_list_int)
print("str:", my_list_str)

[Python] to process time

import time

print(dir(time))    # print components of "time" module
print()

print(time.time(), "\n")    # print time as numeric value calculated from 1970, 1, 1, 00:00:00

t = time.ctime()    # Sat Aug  7 01:19:22 2021
print(t)
time.sleep(1)    # pause for 1 seconds
print(t.split(' ')[-1])    # print 'year' only
print(t.split(' ')[-2].split(':')[-3])    # print "hour" only

now = time.localtime()
filename = "log_%02d%02d%02d_%02d-%02d-%02d.txt" % (now.tm_year, now.tm_mon, now.tm_mday, now.tm_hour, now.tm_min, now.tm_sec)
print("\n", filename)

[Python] how to create a list (array) with a particular quantities of rows and columns

x = 5   # the number of columns

y = 10   # the number of rows

my_list = [[0 for col in range(x)] for row in range(y)] 

print (my_list)

result --------------------------------------------------------------------------------

[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]

[Python] Error: inconsistent use of tabs and spaces in indentation

Consistent indentation is critical in Python.

Not using tab is a way to not create the issue.

When an error of "inconsistent use of tabs and spaces in indentation" appears, in most cases it can be solved by setting "tab replaced by space" option in our code editor.

If the issue still exists, we can easily resolve the issue by using a good Python built-in script called "autopep8".

In your command window, just type like: (assumming that the Python scripts PATH has been registered)

    autopep8  -i  file_name_of_our_code.py