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Hi it's my code for python

I almost finished my project but only one thing left which is most confusing part please help me

I have to find most occurring ending character from a to z

End a b cd ef g j.. rst u VW xy z Count Start a 2.0 0.0 10.0 7.0 49.0 1.0 2.0 2.0 0.0 0.0 ...3.0 11.0 15.0 0.0 0.0 0.0 1.0 11

For instance, output should be like this I have to find a to z.

Words starting with ‘a’ end mostly with ‘O’

Words starting with ‘b’ end mostly with ‘O’

......
No words start with ‘O’(If there's no word in the character from a to z, it should be the same with this)

....

Words starting with ‘z’ end mostly with ‘O’

For example.

from the screenshot above, e seems like the most occurring, so the answer (for a) might be 'e'

In addition,

1. I have to write this output into txt file.

2. Please get rid of decimal points from the code I don't know where they came from...

Thanks!!

-----------------------------------------------------------------------------------------

Here's my code

import numpy as np
import pandas as pd

df_input=pd.read_csv('/Users/Loveyou/Downloads/words_file2.txt',header=None)

df_input.head()

df_output=pd.DataFrame(np.zeros(676,dtype=int).reshape((26,26)),
index = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z'],
columns = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z'])
df_output.index.name="Start"
df_output.columns.name="End"

#Here I have mentioned first i[-1] because in DataFrame first index goes for column
#so we go for End as we mentioned the Row name
for i in df_input[0]:
df_output[i[-1]][i[0]]+=1

#axis=1 is to find colunms and add count
df_output.sum(axis=1)
df_output["Count"] = df_output.sum(axis=1)
#Because axis is default just skip 0 and find raws and add Total
df_output.sum()
df_output.loc["Total", :] = df_output.sum()

df_output.to_csv("Loveyou_project31.csv", mode='w')

df_output

I am not sure for this---->print("Words starting with ‘a’ end mostly with : {}").format(df_output['a'].value_counts())

txt file is here

https://drive.google.com/open?id=1VXtEPNBJ6ypJZ62ypeeWtzS9TjcGhBp4

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Homework Answers

Answer #1 
############## your provided code ############################# 

import numpy as np
import pandas as pd

df_input=pd.read_csv('/Users/Loveyou/Downloads/words_file2.txt',header=None)

df_input.head()

df_output=pd.DataFrame(np.zeros(676, int).reshape((26,26)))
rows = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']
columns = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']
df_output.index.name="Start"
df_output.columns.name="End"
df_output.columns = columns
df_output.index = rows
  
#Here I have mentioned first i[-1] because in DataFrame first index goes for column
#so we go for End as we mentioned the Row name
for i in df_input[0]:
    df_output[i[-1]][i[0]] += 1

#axis=1 is to find colunms and add count
df_output.sum(axis=1)
df_output["Count"] = df_output.sum(axis=1)
#Because axis is default just skip 0 and find rows and add Total
df_output.sum()
df_output.loc["Total", :] = df_output.sum()

df_output.to_csv("Loveyou_project31.csv", mode='w')

############### till here #################################################################

################## additonal code as per your requirements #################################

df_temp = df_output.drop('Total')   # to remove the 'Total' row. 
df_temp = df_temp.drop('Count',axis=1)   # to remove the 'Count' column 
###### changes are not made in actual df_output. we have stored the changes in temporary dataframe df
## we have done this so as to apply 'idxmax' function which returns the index with maximum value
   max_end_list = df_temp.idxmax(axis = 1)
### code for printing which word ends mostly with which word
i=0
for char in rows:
    valid = False
    for j in rows:
        if df_output[j][char] != 0:
            valid = True
            break
    if valid == True:
        print("words starting with " + char + " ends mostly with " + max_end_list[i])
    else:
        print("No words start with " + char)
    i += 1
df_output
##########################################################################

output:

words starting with a ends mostly with e
words starting with b ends mostly with h
words starting with c ends mostly with e
words starting with d ends mostly with e
words starting with e ends mostly with e
words starting with f ends mostly with e
words starting with g ends mostly with s
words starting with h ends mostly with y
words starting with i ends mostly with e
words starting with j ends mostly with n
words starting with k ends mostly with s
words starting with l ends mostly with d
words starting with m ends mostly with e
words starting with n ends mostly with e
words starting with o ends mostly with e
words starting with p ends mostly with e
words starting with q ends mostly with c
words starting with r ends mostly with e
words starting with s ends mostly with e
words starting with t ends mostly with e
words starting with u ends mostly with e
words starting with v ends mostly with e
words starting with w ends mostly with e
No words start with x
words starting with y ends mostly with e
words starting with z ends mostly with h
a b c d e f g h i j ... r s t u v w x y z Count
a 2.0 0.0 10.0 7.0 49.0 1.0 2.0 2.0 0.0 0.0 ... 3.0 11.0 15.0 0.0 0.0 0.0 1.0 11.0 0.0 141.0
b 0.0 0.0 1.0 1.0 4.0 0.0 0.0 6.0 0.0 0.0 ... 0.0 4.0 4.0 0.0 0.0 0.0 0.0 1.0 0.0 27.0
c 1.0 0.0 1.0 5.0 38.0 0.0 4.0 1.0 0.0 0.0 ... 3.0 15.0 22.0 0.0 0.0 0.0 0.0 13.0 0.0 134.0
d 0.0 0.0 3.0 5.0 36.0 0.0 1.0 3.0 0.0 0.0 ... 4.0 6.0 12.0 0.0 0.0 1.0 0.0 6.0 0.0 85.0
e 0.0 0.0 5.0 3.0 24.0 0.0 0.0 1.0 1.0 0.0 ... 3.0 3.0 16.0 0.0 0.0 1.0 0.0 7.0 0.0 72.0
f 0.0 0.0 1.0 5.0 10.0 0.0 0.0 0.0 0.0 0.0 ... 2.0 7.0 4.0 0.0 0.0 0.0 0.0 1.0 0.0 37.0
g 0.0 0.0 0.0 2.0 3.0 0.0 0.0 1.0 0.0 0.0 ... 0.0 4.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 12.0
h 0.0 0.0 0.0 3.0 1.0 0.0 1.0 0.0 0.0 0.0 ... 0.0 4.0 1.0 0.0 0.0 0.0 0.0 5.0 0.0 17.0
i 0.0 0.0 1.0 2.0 42.0 0.0 0.0 0.0 0.0 0.0 ... 3.0 11.0 18.0 0.0 0.0 0.0 0.0 3.0 0.0 88.0
j 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0
k 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
l 0.0 0.0 2.0 4.0 1.0 0.0 0.0 1.0 0.0 0.0 ... 0.0 3.0 3.0 0.0 0.0 0.0 0.0 3.0 0.0 21.0
m 0.0 0.0 0.0 2.0 9.0 0.0 0.0 1.0 0.0 0.0 ... 1.0 9.0 2.0 0.0 0.0 0.0 0.0 2.0 0.0 33.0
n 0.0 0.0 1.0 0.0 5.0 0.0 0.0 0.0 0.0 0.0 ... 1.0 4.0 4.0 0.0 0.0 0.0 0.0 0.0 0.0 17.0
o 0.0 0.0 0.0 0.0 10.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 8.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 22.0
p 2.0 0.0 5.0 4.0 35.0 0.0 1.0 1.0 0.0 0.0 ... 0.0 12.0 10.0 0.0 0.0 0.0 2.0 11.0 0.0 91.0
q 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 7.0
r 0.0 0.0 0.0 3.0 31.0 0.0 0.0 4.0 0.0 0.0 ... 2.0 2.0 7.0 0.0 0.0 0.0 0.0 0.0 0.0 56.0
s 0.0 0.0 3.0 2.0 15.0 0.0 2.0 0.0 0.0 0.0 ... 0.0 10.0 8.0 0.0 0.0 0.0 0.0 7.0 0.0 51.0
t 0.0 0.0 0.0 3.0 9.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 6.0 5.0 0.0 0.0 0.0 0.0 3.0 0.0 31.0
u 1.0 0.0 0.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 2.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 9.0
v 0.0 0.0 1.0 2.0 10.0 0.0 0.0 0.0 0.0 0.0 ... 1.0 5.0 2.0 0.0 0.0 0.0 1.0 3.0 0.0 27.0
w 0.0 0.0 0.0 1.0 2.0 0.0 0.0 1.0 0.0 0.0 ... 1.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 10.0
x 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
y 0.0 0.0 0.0 0.0 1.0 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 1.0
z 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 ... 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.0
Total 6.0 0.0 35.0 55.0 338.0 1.0 11.0 23.0 1.0 0.0 ... 25.0 130.0 136.0 0.0 0.0 3.0 5.0 80.0 0.0 996.0

27 rows × 27 columns

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