I have a pandas dataframe with about 100 columns of following type:
X1 Y1 X2 Y2 X3 Y3
0.78 0.22 0.19 0.42 0.04 0.65
0.43 0.29 0.43 0.84 0.14 0.42
0.57 0.70 0.59 0.86 0.11 0.40
0.92 0.52 0.81 0.33 0.54 1.00
w1here (X,Y) are basically pairs of values
I need to create the following from above.
X Y
0.78 0.22
0.43 0.29
0.57 0.70
0.92 0.52
0.19 0.42
0.43 0.84
0.59 0.86
0.81 0.33
0.04 0.65
0.14 0.42
0.11 0.40
0.54 1.00
i.e. stack all the X columns which are odd numbered and then stack all the Y columns which are even numbered.
I have no clue where to even start. For small number of columns I could easily have use the column names.
You can use lreshape, for column names use list comprehension:
x = [col for col in df.columns if 'X' in col]
y = [col for col in df.columns if 'Y' in col]
df = pd.lreshape(df, {'X': x,'Y': y})
print (df)
X Y
0 0.78 0.22
1 0.43 0.29
2 0.57 0.70
3 0.92 0.52
4 0.19 0.42
5 0.43 0.84
6 0.59 0.86
7 0.81 0.33
8 0.04 0.65
9 0.14 0.42
10 0.11 0.40
11 0.54 1.00
Solution with MultiIndex and stack:
df.columns = [np.arange(len(df.columns)) % 2, np.arange(len(df.columns)) // 2]
df = df.stack().reset_index(drop=True)
df.columns = ['X','Y']
print (df)
X Y
0 0.78 0.22
1 0.19 0.42
2 0.04 0.65
3 0.43 0.29
4 0.43 0.84
5 0.14 0.42
6 0.57 0.70
7 0.59 0.86
8 0.11 0.40
9 0.92 0.52
10 0.81 0.33
11 0.54 1.00
It may also be worth noting that you could just construct a new DataFrame explicitly with the X-Y values. This will most likely be quicker, but it assumes that the X-Y column pairs are the entirety of your DataFrame.
pd.DataFrame(dict(X=df.values[:,::2].reshape(-1),
Y=df.values[:,1::2].reshape(-1)))
Demo
>>> pd.DataFrame(dict(X=df.values[:,::2].reshape(-1),
Y=df.values[:,1::2].reshape(-1)))
X Y
0 0.78 0.22
1 0.19 0.42
2 0.04 0.65
3 0.43 0.29
4 0.43 0.84
5 0.14 0.42
6 0.57 0.70
7 0.59 0.86
8 0.11 0.40
9 0.92 0.52
10 0.81 0.33
11 0.54 1.00
You can use the documented pd.wide_to_long but you will need to use a 'dummy' column to uniquely identify each row. You can drop this column later.
pd.wide_to_long(df.reset_index(),
stubnames=['X', 'Y'],
i='index',
j='dropme').reset_index(drop=True)
X Y
0 0.78 0.22
1 0.43 0.29
2 0.57 0.70
3 0.92 0.52
4 0.19 0.42
5 0.43 0.84
6 0.59 0.86
7 0.81 0.33
8 0.04 0.65
9 0.14 0.42
10 0.11 0.40
11 0.54 1.00
Related
Supposed we have a df with a sum() value in the below DataFrame, thanks so much for #jezrael 's answer here, but we have many different df like below DataFrame with different columns, is it possible to add those three line code in a function?
df.columns=['value_a','value_b','name','up_or_down','difference']
# from here
df.loc['sum'] = df[['value_a','value_b','difference']].sum()
df1 = df[['value_a','value_b','difference']].sum().to_frame().T
df = pd.concat([df1, df], ignore_index=True)
# end here
df
value_a value_b name up_or_down difference
project_name
sum 27.56 25.04 -1.31
2021-project11 0.43 0.48 2021-project11 up 0.05
2021-project1 0.62 0.56 2021-project1 down -0.06
2021-project2 0.51 0.47 2021-project2 down -0.04
2021-porject3 0.37 0.34 2021-porject3 down -0.03
2021-porject4 0.64 0.61 2021-porject4 down -0.03
2021-project5 0.32 0.25 2021-project5 down -0.07
2021-project6 0.75 0.81 2021-project6 up 0.06
2021-project7 0.60 0.60 2021-project7 down 0.00
2021-project8 0.85 0.74 2021-project8 down -0.11
2021-project10 0.67 0.67 2021-project10 down 0.00
2021-project9 0.73 0.73 2021-project9 down 0.00
2021-project11 0.54 0.54 2021-project11 down 0.00
2021-project12 0.40 0.40 2021-project12 down 0.00
2021-project13 0.76 0.77 2021-project13 up 0.01
2021-project14 1.16 1.28 2021-project14 up 0.12
2021-project15 1.01 0.94 2021-project15 down -0.07
2021-project16 1.23 1.24 2021-project16 up 0.01
2022-project17 0.40 0.36 2022-project17 down -0.04
2022-project_11 0.40 0.40 2022-project_11 down 0.00
2022-project4 1.01 0.80 2022-project4 down -0.21
2022-project1 0.65 0.67 2022-project1 up 0.02
2022-project2 0.75 0.57 2022-project2 down -0.18
2022-porject3 0.32 0.32 2022-porject3 down 0.00
2022-project18 0.91 0.56 2022-project18 down -0.35
2022-project5 0.84 0.89 2022-project5 up 0.05
2022-project19 0.61 0.48 2022-project19 down -0.13
2022-project6 0.77 0.80 2022-project6 up 0.03
2022-project20 0.63 0.54 2022-project20 down -0.09
2022-project8 0.59 0.55 2022-project8 down -0.04
2022-project21 0.58 0.54 2022-project21 down -0.04
2022-project10 0.76 0.76 2022-project10 down 0.00
2022-project9 0.70 0.71 2022-project9 up 0.01
2022-project22 0.62 0.56 2022-project22 down -0.06
2022-project23 2.03 1.74 2022-project23 down -0.29
2022-project12 0.39 0.39 2022-project12 down 0.00
2022-project24 1.35 1.55 2022-project24 up 0.20
project25 0.45 0.42 project25 down -0.03
project26 0.53 NaN project26 down NaN
project27 0.68 NaN project27 down NaN
Can I add a function with conditions like below, and our other df values can use the function directly?
def sum_handler(x):
if .......
return .....
elif .......
return .....
else
return .....
Thanks so much for any advice
You could try a different approach for summing up your dataframe like shown in this answer.
df.loc['Total'] = df.sum(numeric_only=True, axis=0)
Since this is a one line of code, there would be no need to create a custom function to do this. But for future referrence, you can create a custom function and apply it to a dataframe like this:
import pandas as pd
def double_columns(df: pd.DataFrame, columns: list[str]):
""" Doubles chosen columns of a dataframe """
df[columns] = df[columns] * 2
return df
df = pd.DataFrame({'col1': [1,2], 'col2': [2,3]})
df = double_columns(df, ['col1'])
print(df)
would return
col1 col2
0 2 2
1 4 3
Supposed I have a df as below, how to add a sum() value in below DataFrame?
df.columns=['value_a','value_b','name','up_or_down','difference']
df
value_a value_b name up_or_down difference
project_name
# sum 27.56 25.04 sum down -1.31
2021-project11 0.43 0.48 2021-project11 up 0.05
2021-project1 0.62 0.56 2021-project1 down -0.06
2021-project2 0.51 0.47 2021-project2 down -0.04
2021-porject3 0.37 0.34 2021-porject3 down -0.03
2021-porject4 0.64 0.61 2021-porject4 down -0.03
2021-project5 0.32 0.25 2021-project5 down -0.07
2021-project6 0.75 0.81 2021-project6 up 0.06
2021-project7 0.60 0.60 2021-project7 down 0.00
2021-project8 0.85 0.74 2021-project8 down -0.11
2021-project10 0.67 0.67 2021-project10 down 0.00
2021-project9 0.73 0.73 2021-project9 down 0.00
2021-project11 0.54 0.54 2021-project11 down 0.00
2021-project12 0.40 0.40 2021-project12 down 0.00
2021-project13 0.76 0.77 2021-project13 up 0.01
2021-project14 1.16 1.28 2021-project14 up 0.12
2021-project15 1.01 0.94 2021-project15 down -0.07
2021-project16 1.23 1.24 2021-project16 up 0.01
2022-project17 0.40 0.36 2022-project17 down -0.04
2022-project_11 0.40 0.40 2022-project_11 down 0.00
2022-project4 1.01 0.80 2022-project4 down -0.21
2022-project1 0.65 0.67 2022-project1 up 0.02
2022-project2 0.75 0.57 2022-project2 down -0.18
2022-porject3 0.32 0.32 2022-porject3 down 0.00
2022-project18 0.91 0.56 2022-project18 down -0.35
2022-project5 0.84 0.89 2022-project5 up 0.05
2022-project19 0.61 0.48 2022-project19 down -0.13
2022-project6 0.77 0.80 2022-project6 up 0.03
2022-project20 0.63 0.54 2022-project20 down -0.09
2022-project8 0.59 0.55 2022-project8 down -0.04
2022-project21 0.58 0.54 2022-project21 down -0.04
2022-project10 0.76 0.76 2022-project10 down 0.00
2022-project9 0.70 0.71 2022-project9 up 0.01
2022-project22 0.62 0.56 2022-project22 down -0.06
2022-project23 2.03 1.74 2022-project23 down -0.29
2022-project12 0.39 0.39 2022-project12 down 0.00
2022-project24 1.35 1.55 2022-project24 up 0.20
project25 0.45 0.42 project25 down -0.03
project26 0.53 NaN project26 down NaN
project27 0.68 NaN project27 down NaN
I tried
df.sum().columns=['value_a_sun','value_b_sum','difference_sum']
And I would like to add below sum value in the above column value,
sum 27.56 25.04 sum down -1.31
But I got AttributeError: 'Series' object has no attribute 'column', how to fix this? Thanks so much for any advice.
Filter columns names in subset by [] before sum and assign for new row in DataFrame.loc:
df.loc['sum'] = df[['value_a','value_b','difference']].sum()
For first line:
df1 = df[['value_a','value_b','difference']].sum().to_frame().T
df = pd.concat([df1, df], ignore_index=True)
I have the following dataframe:
>>> mes1 mes2 mes3 mes4 mes5
A1 0.45 0.21 0.53 0.33 0.11
A2 0.44 0.32 0.11 0.38 0.91
A3 0.78 0.31 0.53 0.32 0.14
A4 0.12 0.33 0.56 0.43 0.12
posUp 0.52 0.40 0.62 0.48 0.54
posDown 0.32 0.15 0.45 0.24 0.05
I want to filer my dataframe, so I'll be left only with rows that their value is between the value of "posUp" and "posDown" for all the columns, so the result should be:
>>> mes1 mes2 mes3 mes4 mes5
A1 0.45 0.21 0.53 0.33 0.11
posUp 0.52 0.40 0.62 0.48 0.54
posDown 0.32 0.15 0.45 0.24 0.05
I have tried to do it by slicing the dataframe into series and then put condition like this:
for i in df:
db=df[i]
vmin=db.loc['posUp']
vmax=db.loc['posDown']
db=db[(db>vmin)&(db<vmax)]
and then I wanted to drop the rows that will not be found in the last db filter, but it didn't filter anything and when I print db I got "Series([],Name: ..."
Beside that, I believe there is more convenient / efficient way to do it than for loops.
So my end goal is to have only the rows that in all the columns, their value is between posUp and posDown.
Try with le and ge:
mask = (df.le(df.loc['posUp']) # compare with `posUp` row-wise
& df.ge(df.loc['posDown']) # compare with `posDown` row-wise
).all(1) # check for all True along the rows
df[mask]
Output:
mes1 mes2 mes3 mes4 mes5
A1 0.45 0.21 0.53 0.33 0.11
posUp 0.52 0.40 0.62 0.48 0.54
posDown 0.32 0.15 0.45 0.24 0.05
You can try all after sub . PS : A3 should not included since mes1 is 0.78
out = df[(df.sub(df.loc['posUp']).le(0) & df.sub(df.loc['posDown']).ge(0)).all(1)]
Out[107]:
mes1 mes2 mes3 mes4 mes5
A1 0.45 0.21 0.53 0.33 0.11
posUp 0.52 0.40 0.62 0.48 0.54
posDown 0.32 0.15 0.45 0.24 0.05
This question already has answers here:
Python - Drop row if two columns are NaN
(5 answers)
How to drop column according to NAN percentage for dataframe?
(4 answers)
Closed 4 years ago.
I have the following dataset:
A B C D
0 0.46 0.23 NaN 0.41
1 0.65 0.48 0.07 0.15
2 NaN 1.00 0.79 0.09
3 0.50 0.97 0.07 0.55
4 0.45 0.44 0.23 0.41
5 NaN 0.39 NaN 0.31
6 0.32 0.87 0.73 0.57
7 0.82 0.67 0.73 0.19
8 0.65 NaN NaN 0.81
9 0.36 0.23 1.00 0.51
I would like to delete rows in which there are 2 or more values missing (or specify as more than 50% missing?), therefore I would like to delete rows 5 and 8 and get the following output:
A B C D
0 0.46 0.23 NaN 0.41
1 0.65 0.48 0.07 0.15
2 NaN 1.00 0.79 0.09
3 0.50 0.97 0.07 0.55
4 0.45 0.44 0.23 0.41
6 0.32 0.87 0.73 0.57
7 0.82 0.67 0.73 0.19
9 0.36 0.23 1.00 0.51
Thank you.
I'm working with a frame like
df = pd.DataFrame({
'G1':[1.00,0.69,0.23,0.22,0.62],
'G2':[0.03,0.41,0.74,0.35,0.62],
'G3':[0.05,0.40,0.15,0.32,0.19],
'G4':[0.30,0.20,0.51,0.70,0.67],
'G5':[0.40,0.36,0.88,0.10,0.19]
})
and I want to manipulate it so that the columns are pairwise permutations of the current columns e.g. all columns are now 10 elements long and for example column 'G1:G2' would have column 'G2' appended to column 'G1'. I have attached a mock-up pic. Note that the pic has named indices unlike the above example code. I can work with or without the indices.
How could I approach this? I can make a function to act on each column, but I think the function would have to return a data frame made by concatenation with all other columns. Not sure what that would look like.
I'd do it like this
from itertools import permutations
l1, l2 = map(list, zip(*permutations(range(len(df.columns)), 2)))
v = df.values
pd.DataFrame(
np.vstack([v[:, l1], v[:, l2]]),
list(map('S{}'.format, range(1, len(df) + 1))) * 2,
df.columns.values[l1] + ':' + df.columns.values[l2]
)
Here is one way, although I suspect there might also be a way to do this directly in pandas
from itertools import permutations
'''Get all the column permutations'''
lst = [x for x in permutations(df.columns, 2)]
'''Create a list of columns names'''
names = [x[0]+'_'+x[1] for x in lst]
'''Create the new arrays by vertically stacking pairs of column values'''
cols = [np.vstack((df[x[0]].values,df[x[1]].values)).ravel() for x in lst]
'''Create a dictionary with column names as keys and the arrays as values'''
d = dict(zip(names, cols))
'''Create new dataframe from dict'''
df2 = pd.DataFrame(d)
df2
G1_G2 G1_G3 G1_G4 G1_G5 G2_G1 G2_G3 G2_G4 G2_G5 G3_G1 G3_G2 \
0 1.00 1.00 1.00 1.00 0.03 0.03 0.03 0.03 0.05 0.05
1 0.69 0.69 0.69 0.69 0.41 0.41 0.41 0.41 0.40 0.40
2 0.23 0.23 0.23 0.23 0.74 0.74 0.74 0.74 0.15 0.15
3 0.22 0.22 0.22 0.22 0.35 0.35 0.35 0.35 0.32 0.32
4 0.62 0.62 0.62 0.62 0.62 0.62 0.62 0.62 0.19 0.19
5 0.03 0.05 0.30 0.40 1.00 0.05 0.30 0.40 1.00 0.03
6 0.41 0.40 0.20 0.36 0.69 0.40 0.20 0.36 0.69 0.41
7 0.74 0.15 0.51 0.88 0.23 0.15 0.51 0.88 0.23 0.74
8 0.35 0.32 0.70 0.10 0.22 0.32 0.70 0.10 0.22 0.35
9 0.62 0.19 0.67 0.19 0.62 0.19 0.67 0.19 0.62 0.62
This is part of the output
To avoid creating the lists and use the fact that itertools.permutations is a generator:
d = dict((x[0]+'_'+x[1] , np.vstack((df[x[0]].values,df[x[1]].values)).ravel())
for x in permutations(df.columns, 2))
df2 = pd.DataFrame(d)