Develop Reference

Is there any way to set row to column-row in DataFrame? or How to get DataFrame from Excel file with setting any index row to column_row?

Q1) Is there any way to set row to column-row in DataFrame?
(DF) (DF)
A B C D a b c d
0 a b c d pandas function 0 4 5 3 6
1 4 5 3 6 ==========================> 1 3 2 5 3
2 3 2 5 3 0-idx row to columns-row 2 4 7 9 0
3 4 7 9 0
Q2) How to get DataFrame from Excel file with setting any index row to column_row?
(EXCEL or CSV) (DF)
A B C D a b c d
0 a b c d pd.read_excel() 0 4 5 3 6
1 4 5 3 6 ==========================> 1 3 2 5 3
2 3 2 5 3 0-idx row to columns-row 2 4 7 9 0
3 4 7 9 0

You can try it:
import pandas as pd
data={"A":['a',4,3,4],"B":['b',5,2,7],"C":['c',3,5,9],"D":['d',6,3,0]}
df=pd.DataFrame(data)
#define the column name from line index 0
df.columns=df.iloc[0].tolist()
#remove the line index 0
df = df.drop(0)
result:
a b c d
1 4 5 3 6
2 3 2 5 3
3 4 7 9 0

This would do the job:
new_header = df.iloc[0] #first_row
df = df[1:] #remaining_dataframe
df.columns = new_header

Pandas take the line value below

There is such a model of real data:
C S E D
1 1 3 0 0
2 1 5 0 0
3 1 6 0 0
4 2 1 0 0
5 2 3 0 0
6 2 7 0 0
С - category, S - start, E - end, D - delta
Using pandas, you need to enter the value of column S with the condition id = id+1 in column E, and the last value of category E is equal to the value from column S of the same row
It turns out:
C S E D
1 1 3 5 0
2 1 5 6 0
3 1 6 6 0
4 2 1 3 0
5 2 3 7 0
6 2 7 7 0
And then subtract S from E and put it in D. This, in principle, is easy. The difficulty is filling in column E
The result is this:
C S E D
1 1 3 5 2
2 1 5 6 1
3 1 6 6 0
4 2 1 3 2
5 2 3 7 4
6 2 7 7 0

Use DataFrameGroupBy.shift with replace last missing values by original with Series.fillna and then only subtract for column D:
df['E'] = df.groupby('C')['S'].shift(-1).fillna(df['S']).astype(int)
df['D'] = df['E'] - df['S']
Or if use DataFrame.assign is necessary use lambda function for use counted values of E column:
df = df.assign(E = df.groupby('C')['S'].shift(-1).fillna(df['S']).astype(int),
D = lambda x: x['E'] - x['S'])
print (df)
C S E D
1 1 3 5 2
2 1 5 6 1
3 1 6 6 0
4 2 1 3 2
5 2 3 7 4
6 2 7 7 0

Indexing new dataframes into new columns with pandas

I need to create a new dataframe from an existing one by selecting multiple columns, and appending those column values to a new column with it's corresponding index as a new column
So, lets say I have this as a dataframe:
A B C D E F
0 1 2 3 4 0
0 7 8 9 1 0
0 4 5 2 4 0
Transform into this by selecting columns B through E:
A index_value
1 1
7 1
4 1
2 2
8 2
5 2
3 3
9 3
2 3
4 4
1 4
4 4
So, for the new dataframe, column A would be all of the values from columns B through E in the old dataframe, and column index_value would correspond to the index value [starting from zero] of the selected columns.
I've been scratching my head for hours. Any help would be appreciated, thanks!
Python3, Using pandas & numpy libraries.

#Another way
A B C D E F
0 0 1 2 3 4 0
1 0 7 8 9 1 0
2 0 4 5 2 4 0
# Select columns to include
start_colum ='B'
end_column ='E'
index_column_name ='A'
#re-stack the dataframe
df = df.loc[:,start_colum:end_column].stack().sort_index(level=1).reset_index(level=0, drop=True).to_frame()
#Create the "index_value" column
df['index_value'] =pd.Categorical(df.index).codes+1
df.rename(columns={0:index_column_name}, inplace=True)
df.set_index(index_column_name, inplace=True)
df
index_value
A
1 1
7 1
4 1
2 2
8 2
5 2
3 3
9 3
2 3
4 4
1 4
4 4

This is just melt
df.columns = range(df.shape[1])
s = df.melt().loc[lambda x : x.value!=0]
s
variable value
3 1 1
4 1 7
5 1 4
6 2 2
7 2 8
8 2 5
9 3 3
10 3 9
11 3 2
12 4 4
13 4 1
14 4 4

Try using:
df = pd.melt(df[['B', 'C', 'D', 'E']])
# Or df['variable'] = df[['B', 'C', 'D', 'E']].melt()
df['variable'].shift().eq(df['variable'].shift(-1)).cumsum().shift(-1).ffill()
print(df)
Output:
variable value
0 1.0 1
1 1.0 7
2 1.0 4
3 2.0 2
4 2.0 8
5 2.0 5
6 3.0 3
7 3.0 9
8 3.0 2
9 4.0 4
10 4.0 1
11 4.0 4

Pad dataframe discontinuous column

I have the following dataframe:
Name B C D E
1 A 1 2 2 7
2 A 7 1 1 7
3 B 1 1 3 4
4 B 2 1 3 4
5 B 3 1 3 4
What I'm trying to do is to obtain a new dataframe in which, for rows with the same "Name", the elements in the "B" column are continuous, hence in this example for rows with "Name" = A, the dataframe would have to be padded with elements ranging from 1 to 7, and the values for columns C, D, E should be 0.
Name B C D E
1 A 1 2 2 7
2 A 2 0 0 0
3 A 3 0 0 0
4 A 4 0 0 0
5 A 5 0 0 0
6 A 6 0 0 0
7 A 7 0 0 0
8 B 1 1 3 4
9 B 2 1 5 4
10 B 3 4 3 6
What I've done so far is to turn the B column values for the same "Name" into continuous values:
new_idx = df_.groupby('Name').apply(lambda x: np.arange(x.index.min(), x.index.max() + 1)).apply(pd.Series).stack()
and reindexing the original (having set B as the index) df using this new Series, but I'm having trouble reindexing using duplicates. Any help would be appreciated.

You can use:
def f(x):
a = np.arange(x.index.min(), x.index.max() + 1)
x = x.reindex(a, fill_value=0)
return (x)
new_idx = (df.set_index('B')
.groupby('Name')
.apply(f)
.drop('Name', 1)
.reset_index()
.reindex(columns=df.columns))
print (new_idx)
Name B C D E
0 A 1 2 2 7
1 A 2 0 0 0
2 A 3 0 0 0
3 A 4 0 0 0
4 A 5 0 0 0
5 A 6 0 0 0
6 A 7 1 1 7
7 B 1 1 3 4
8 B 2 1 3 4
9 B 3 1 3 4

Add a name to pandas dataframe index

As the picture shows , how can I add a name to index in pandas dataframe?And when added it should be like this:

You need set index name:
df.index.name = 'code'
Or rename_axis:
df = df.rename_axis('code')
Sample:
np.random.seed(100)
df = pd.DataFrame(np.random.randint(10,size=(5,5)),columns=list('ABCDE'),index=list('abcde'))
print (df)
A B C D E
a 8 8 3 7 7
b 0 4 2 5 2
c 2 2 1 0 8
d 4 0 9 6 2
e 4 1 5 3 4
df.index.name = 'code'
print (df)
A B C D E
code
a 8 8 3 7 7
b 0 4 2 5 2
c 2 2 1 0 8
d 4 0 9 6 2
e 4 1 5 3 4
df = df.rename_axis('code')
print (df)
A B C D E
code
a 8 8 3 7 7
b 0 4 2 5 2
c 2 2 1 0 8
d 4 0 9 6 2
e 4 1 5 3 4