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How do I apply Sympy.Solve to solve a two-sided function on each row in a database?

I am attempting to apply the sympy.solve function to each row in a DataFrame using each column a different variable. I have managed to solve for the undefined variable I need to calculate — λ — using the following code:

from sympy import symbols, Eq, solve
import math

Tmin = 33.2067
Tmax = 42.606
D = 19.5526
tmin = 6
tmax = 14
pi = math.pi

λ = symbols('λ')

lhs = D
eq1 = (((Tmax-Tmin)/2) * (λ-tmin) * (1-(2/pi))) + (((Tmax-Tmin)/2)*(tmax-λ)*(1+(2/pi))) - (((Tmax-Tmin)/2)*(tmax-tmin))

eq1 = Eq(lhs, rhs) 
lam = solve(eq1)

However, I need to apply this function to every row in a DataFrame and output the result as its own column. The DataFrame is formatted as follows:

import pandas as pd

data = [[6, 14, 33.2067, 42.606, 19.5526], [6, 14, 33.4885, 43.0318, -27.9222]]
df = pd.DataFrame(data, columns=['tmin', 'tmax', 'Tmin', 'Tmax', 'D'])

I have searched for how to do this, but am not sure how to proceed. I managed to find similar questions wherein the answers discussed lambdifying the equation, but I wasn’t sure how to lambdify a two-sided equation and then apply it to my DataFrame, and my math skills aren’t strong enough to isolate λ and place it on the left side of the equation so that I don’t have to lambdify a two-sided equation. Any help here would be appreciated.



You can use the apply method of a dataframe in order to apply a numerical function to each row. But first we need to create the numerical function: we are going to do that with lambdify:

from sympy import symbols, Eq, solve, pi, lambdify
import pandas as pd

# create the necessary symbols
Tmin, Tmax, D, tmin, tmax = symbols("T_min, T_max, D, t_min, t_max")
λ = symbols('λ')

# create the equation and solve for λ
lhs = D
rhs = (((Tmax-Tmin)/2) * (λ-tmin) * (1-(2/pi))) + (((Tmax-Tmin)/2)*(tmax-λ)*(1+(2/pi))) - (((Tmax-Tmin)/2)*(tmax-tmin))
eq1 = Eq(lhs, rhs) 
# solve eq1 for λ: take the first (and only) solution
λ_expr = solve(eq1, λ)[0]
# out: (-pi*D + T_max*t_max + T_max*t_min - T_min*t_max - T_min*t_min)/(2*(T_max - T_min))

# convert λ_expr to a numerical function so that it can
# be quickly evaluated. Essentialy, creates:
# λ_func(tmin, tmax, Tmin, Tmax, D)
# NOTE: for simplicity, let's order the symbols like the
# columns of the dataframe
λ_func = lambdify([tmin, tmax, Tmin, Tmax, D], λ_expr)
# We are going to use df.apply to apply the function to each row
# of the dataframe. However, pandas will pass into the current row
# as the argument. For example:
# row = [val_tmin, val_tmax, val_Tmin, val_Tmax, val_D]
# Hence, we need a wrapper function to unpack the row to the
# arguments required by λ_func
wrapper_func = lambda row: λ_func(*row)

# create the dataframe
data = [[6, 14, 33.2067, 42.606, 19.5526], [6, 14, 33.4885, 43.0318, -27.9222]]
df = pd.DataFrame(data, columns=['tmin', 'tmax', 'Tmin', 'Tmax', 'D'])

# apply the function to each row
print(df.apply(wrapper_func, axis=1))
# 0     6.732400044759731
# 1    14.595903848357743
# dtype: float64