Selecting data from a pandas DataFrame

Question

I have defined a pandas DataFrame, given the number of rows (index) and columns. I perform a series of operations and store the data in such DataFrame. The code that makes this operation is the next one:

import math
import numpy as np
import pandas as pd

sens_fac = [0.8, 1, 1.2]

A = 13;
B = 5;
C = 7/2;
D = 3*1.2;
par = [A,B,C,D]
data = pd.DataFrame(index=range(len(sens_fac)),columns=range(len(par)))

for i in range(len(par)):
    factors = [1, 1, 1, 1]
    for j in range(len(sens_fac)):
        factors[i] = sens_fac[j]
        print(factors)
        x=25
        t1 = np.log(x)**math.sin(x/(A*factors[0]))
        t2 = (B*factors[1])*math.sqrt(x)
        t3 = (factors[2]*C)**math.exp(1/x)
        t4 = x/(factors[3]*D)*2
        res = t1 + t2 + t3 + t4
        data[i][j] = res

The problem is when I try to select a specific element of such DataFrame. For example, if I print data, it would be a DataFrame of 3 rows and 4 columns. But when I try to select the element of the third row and fourth column (data[2][3]), I get an error. On the other hand, if I select the element asdata[3][2], it gives me the number I am looking for, but I understand that data[3][2] would be the fourth row and third column, which is an element that does not exist.

Answer 1

The data frame values can be accessed using explicit indexing(loc), implicit indexing (iloc). To be more clear: suppose column 3 has the name 'qwe', and the index of row 2 will be 'c'. This is called explicit reference to indexes.

data.loc['c', 'qwe']

Implicitly , you can apply like this:

data.iloc[2, 3]

But, if you will use a slice. Then with explicit indexing, there will be one more value, since with explicit indexing, the slice occurs inclusive.

print(data.loc[:2, 3])
print(data.iloc[:2, 3])
"""
0    49.0406
1    45.5684
2    43.2536
Name: 3, dtype: object
0    49.0406
1    45.5684
Name: 3, dtype: object
"""

Since you have indexes (rows) in strict numbering, using an explicit and implicit index will lead to the same result.

print(data.loc[2, 3])#43.2535547215997
print(data.iloc[2, 3])#43.2535547215997

iloc, loc and at, iat the difference is described here

For single values, it is assumed that faster at, iat.