How do I create a Dataframe_new in python from an existing Dataframe_old.

How do I create a Dataframe_new in python from an existing Dataframe_old.

By : user2171866
Date : October 25 2020, 09:10 AM
I hope this helps you . I would create dummies of the variables and then group the data by question_id and sum up the columns:
code :
In [1]: import pandas as pd

In [2]: df = pd.read_csv('~/Desktop/stackoverflow_data.tsv', sep='\t')

In [3]: df
   user_id  question_id scars fingernail missing_fin
0     1015            1    s2         f5         mf0
1     1016            1    s2         f3         mf0
2     1017            1    s2         f5         mf0
3     1015            2    s1         f1         mf1
4     1016            2    s1         f1         mf1
5     1017            2    s2         f2         mf1

In [4]: df = pd.get_dummies(df, columns=['scars', 'fingernail', 'missing_fin'])

In [5]: df.drop('user_id', axis=1, inplace=True)

In [6]: df_new = df.groupby('question_id').sum(axis=0)

In [7]: df_new
             scars_s1  scars_s2  fingernail_f1  fingernail_f2  fingernail_f3  \
1                   0         3              0              0              1   
2                   2         1              2              1              0   

             fingernail_f5  missing_fin_mf0  missing_fin_mf1  
1                        2                3                0  
2                        0                0                3  

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Using Python to create a new Numpy array from an existing one

Using Python to create a new Numpy array from an existing one

By : Isharma
Date : March 29 2020, 07:55 AM
Hope this helps Following my snide comment about homework:
Looking at your Java code, I think this is what you want?
code :
import numpy as np

data = np.array([10, 15, 5, 25])
diff = np.abs(data[:-1] - data[1:])

print diff
array([ 5, 10, 20])
print data[:-1]
array([10, 15,  5])
print data[1:]
array([15,  5, 25])
new = np.empty(shape = data.shape[0]-1)

for i in range(0, new.shape[0]):
    new[i] = np.abs(data[i+1] - data[i])
import numpy as np
data =  np.array([10, 15, 5, 25])
data_2d = np.repeat(data,2).reshape(-1,2) #make some 2d data
data_2d[:,1] = data_2d[:,1] + 100 #make the y axis different so we can tell them apart easier

print data_2d
[[ 10 110]
 [ 15 115]
 [  5 105]
 [ 25 125]]

Making a new array to store the results, copying over the Y values.
The X values we will change later. Note that not using the .copy() 
method would create a VIEW of data_2d, so when we change new,
data_2d would change as well.

new = data_2d[:-1,:].copy()

print new.shape
(3,2) # 3 here is the number of elements per axis, 2 is the number of axes. 

for i in range(0,data_2d.shape[0]-1): # looping the X axis
   new[i,0] = np.abs(data_2d[i+1,0] - data_2d[i,0]) # referencing the X axis explicitly

print new
[[  5 110]
 [ 10 115]
 [ 20 105]]
Create unique ID from the existing two columns, python

Create unique ID from the existing two columns, python

By : Kanyinsola Ati-John
Date : March 29 2020, 07:55 AM
help you fix your problem You can use pandas.
Assuming your data is in a csv file, read in the data:
code :
import pandas as pd 

df = pd.read_csv('data.csv', delim_whitespace=True)
df['unique_id'] = df.hh_id.astype(str) + '_' + df.pno.astype(str)
    hh_id  pno unique_id
0  682138    1  682138_1
1  365348    1  365348_1
2  365348    2  365348_2
df.to_csv('out.csv', index=False)
Create method from existing python code

Create method from existing python code

By : zgy
Date : March 29 2020, 07:55 AM
I wish this helpful for you Identify the variables that can change and make those arguments to a function. Leave the printing and exception handling outside of the function unless you can do something sensible with the exception:
code :
def fn(baseDN, searchScope, adname, retrieveAttributes):
    ldap_result_id = l.search(baseDN, searchScope, get_searchFilter(adname), 
    result_set = []
    while 1:
        result_type, result_data = l.result(ldap_result_id, 0)
        if (result_data == []):
            ## you could do whatever you want with the individual entry
            ## The appending to list is just for illustration.
            if result_type == ldap.RES_SEARCH_ENTRY:
    return result_set

baseDN = ???
searchScope = ???
adname = ???
retrieveAttributes = ???
    for x in fn(baseDN, searchScope, adname, retrieveAttributes):
        print x
except ldap.LDAPError, e:
    print e
    print ldap.LDAPError
Create outlines/TOC for existing PDF in Python

Create outlines/TOC for existing PDF in Python

By : Peter Webb
Date : March 29 2020, 07:55 AM
With these it helps We had a similar problem in WeasyPrint: cairo produces the PDF files but does not support bookmarks/outlines or hyperlinks. In the end we bit the bullet, read the PDF spec, and did it ourselves.
WeasyPrint’s pdf.py has a simple PDF parser and writer that can add/override PDF "objects" to an existing documents. It uses the PDF "update" mechanism and only append at the end of the file.
Boost.Python create new reference to existing Python object from C++

Boost.Python create new reference to existing Python object from C++

By : Jakub Płużek
Date : March 29 2020, 07:55 AM
To fix this issue To accomplish this, one must modify another frame on the call stack. Be warned, this is dependent on the Python implementation. For example, in Python 2.7, the inspect module and sys.settrace() can be used to modify locals() on a specific frame.
I would highly recommend using a Python solution, as was done in this answer, and monkey patch the desired class' __init__ function. For instance, the following would patch the Spam class to insert a variable named last_spam that references the newly constructed Spam instance into the caller's frame:
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