Please begging you to help me   I have a data-frame that I am trying to transpose, My original data-frame looks like this in excel           I was able to partially transpose the data and index the Locations, which I would like to be my first column and not the index. df_transposed = df.T df_transposed         df_transposed.reset_index(drop = True, inplace = True)         I would like to have the Location, Assessment, Subject, Grade, Percent Met, Total Met as columns in the dataframe.

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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Please begging you to help me
 

I have a data-frame that I am trying to transpose, My original data-frame looks like this in excel

 

 

 

 

 

I was able to partially transpose the data and index the Locations, which I would like to be my first column and not the index.

df_transposed = df.T

df_transposed

 

 

 

 

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

 

 

 

 

I would like to have the Location, Assessment, Subject, Grade, Percent Met, Total Met as columns in the dataframe.

 

 

 
Assessment
3
M-STEP
4
M-STEP
5
M-STEP
6
M-STEP
7
M-STEP
8
M-STEP
9
M-STEP
10 M-STEP
11
M-STEP
12 M-STEP
M.STED
df_transposed= df.T
Show Transcribed Text
df transposed
Subject
English
English
English
English
English
English
0
Mathematics
Mathematics
Mathematics
Mathematics
1
Mathematice
Location Assessment
Grade
Grade 3
Grade 4
Grade 5
Grade 6
Grade 7
2 0 Grade
Grade 8
Grade 3
Grade 4
Grade 5
Grade 6
Gmde 7
Muskegon
5 Area ISD Total Met
(61)
Total Met
3
0 1 2 3 4 5 6 7
Ć
M-M- M- M M. M
STEP STEP STEP STEP STEP STEP
Grade Grade Grade Grade Grade Grade
3 4 5 6 7 8
3
Statewide Total Met 3666 3803 3978 3379 3544 3676
3666
3803
3978
3379
4
Statewide Percent Met 19.2% 20.2% 20.7% 17.7 % 19.2% 20.1%
3544
3676
3688
2858
1808
1856
1950
I was able to partially transpose the data and
index the Locations, which I would like to be
my first column and not the index.
df_transposed = df.T
df_transposed
8
Percent Met
M-STEP M-STEP
Grade 3 Grade 4
19.3%
19.20%
20.20%
20.70%
17.70%
3688 2858
19.20%
20.10%
15.2%
19.30%
15,20%
9.40%
9.70%
10 100%
9 178 179 180
↑ VO
H
Total Met
0 Subject English English English English English English Mathematics Mathematics Writing Writing Writing W
THE
H
THE
43
48
53
<10 <10 <10
41
63
43 48 53 41 63 47 63 49 <10 <10 <10
47
63
49
24
20
21
WIDA WIDA WIDA 1
Grade Grade Grade (
6 7 8
Transcribed Image Text:Assessment 3 M-STEP 4 M-STEP 5 M-STEP 6 M-STEP 7 M-STEP 8 M-STEP 9 M-STEP 10 M-STEP 11 M-STEP 12 M-STEP M.STED df_transposed= df.T Show Transcribed Text df transposed Subject English English English English English English 0 Mathematics Mathematics Mathematics Mathematics 1 Mathematice Location Assessment Grade Grade 3 Grade 4 Grade 5 Grade 6 Grade 7 2 0 Grade Grade 8 Grade 3 Grade 4 Grade 5 Grade 6 Gmde 7 Muskegon 5 Area ISD Total Met (61) Total Met 3 0 1 2 3 4 5 6 7 Ć M-M- M- M M. M STEP STEP STEP STEP STEP STEP Grade Grade Grade Grade Grade Grade 3 4 5 6 7 8 3 Statewide Total Met 3666 3803 3978 3379 3544 3676 3666 3803 3978 3379 4 Statewide Percent Met 19.2% 20.2% 20.7% 17.7 % 19.2% 20.1% 3544 3676 3688 2858 1808 1856 1950 I was able to partially transpose the data and index the Locations, which I would like to be my first column and not the index. df_transposed = df.T df_transposed 8 Percent Met M-STEP M-STEP Grade 3 Grade 4 19.3% 19.20% 20.20% 20.70% 17.70% 3688 2858 19.20% 20.10% 15.2% 19.30% 15,20% 9.40% 9.70% 10 100% 9 178 179 180 ↑ VO H Total Met 0 Subject English English English English English English Mathematics Mathematics Writing Writing Writing W THE H THE 43 48 53 <10 <10 <10 41 63 43 48 53 41 63 47 63 49 <10 <10 <10 47 63 49 24 20 21 WIDA WIDA WIDA 1 Grade Grade Grade ( 6 7 8
24] df_transposed.reset_index (drop = True, inplace
df_transposed.head
> <bound method NDFrame.head of
0
ܘ ܝ ܚ ܚ ܗ ܗ
3
a SAWNHO
1
2
3
4
5
6
index
Location
0
0
Statewide
Statewide
Muskegon Area ISD (61)
Muskegon Area ISD (61)
=
True)
0
M-STEP
Assessment M-STEP
Subject English English
Grade 4
Grade
Total Met
Percent Met
Grade 3
3666
19.2%
43
10.8%
Total Met
Percent Met
3803
20.2%
48
14.0%
1
2
3 \
Transcribed Image Text:24] df_transposed.reset_index (drop = True, inplace df_transposed.head > <bound method NDFrame.head of 0 ܘ ܝ ܚ ܚ ܗ ܗ 3 a SAWNHO 1 2 3 4 5 6 index Location 0 0 Statewide Statewide Muskegon Area ISD (61) Muskegon Area ISD (61) = True) 0 M-STEP Assessment M-STEP Subject English English Grade 4 Grade Total Met Percent Met Grade 3 3666 19.2% 43 10.8% Total Met Percent Met 3803 20.2% 48 14.0% 1 2 3 \
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