This is a code task requiring DataFrame creation with specified row and column labels.

A DataFrame with custom row and column labels is best created by passing a dictionary (where keys become column labels) along with an explicit index parameter for row labels. Each dictionary value is a list of data for that column, in the order matching the index.

import pandas as pd

# Define the data as a dictionary with years as keys
data = {
    2014: [100.5, 150.8, 200.9, 30000, 40000],
    2015: [12000, 18000, 22000, 30000, 45000],
    2016: [20000, 50000, 70000, 100000, 125000],
    2017: [50000, 60000, 70000, 80000, 90000]
}

# Define the row labels (sales person names)
index = ['Madhu', 'Kusum', 'Kinshuk', 'Ankit', 'Shruti']

# Create the DataFrame
Sales = pd.DataFrame(data, index=index)

print(Sales)

Expected Output:

          2014   2015    2016   2017
Madhu    100.5  12000   20000  50000
Kusum    150.8  18000   50000  60000
Kinshuk  200.9  22000   70000  70000
Ankit   30000.0 30000  100000  80000
Shruti  40000.0 45000  125000  90000

Key points: …

Why a DataFrame?

A DataFrame is the right structure here because we have tabular data with meaningful labels on both axes. The years (2014–2017) naturally form column headers, and the sales person names serve as row identifiers. This two-dimensional labeled structure makes it trivial to query sales by person, by year, or both — far more intuitive than nested lists or a plain NumPy array.

The Construction Strategy

Pandas offers several ways to build a DataFrame. For data already organized in rows (one row per sales person), the dictionary-of-lists approach is cleanest: each key is a column name (year), and its value is a list of sales figures in the order of the sales persons. We then pass the index parameter to label the rows.

import pandas as pd

# Define the sales data as a dictionary: keys are years (columns), values are lists of sales figures
data = {
    2014: [100.5, 150.8, 200.9, 30000, 40000],
    2015: [12000, 18000, 22000, 30000, 45000],
    2016: [20000, 50000, 70000, 100000, 125000],
    2017: [50000, 60000, 70000, 80000, 90000]
}

# Row labels (sales person names) in the same order as the data lists
index = ['Madhu', 'Kusum', 'Kinshuk', 'Ankit', 'Shruti']

# Create the DataFrame
Sales = pd.DataFrame(data, index=index)

print(Sales)

Expected Output:

          2014   2015    2016   2017
Madhu     100.5  12000   20000  50000
Kusum     150.8  18000   50000  60000
Kinshuk   200.9  22000   70000  70000
Ankit   30000.0  30000  100000  80000
Shruti  40000.0  45000  125000  90000

Key Points in the Code

data dictionary: Each year is a key, and the corresponding list contains sales figures for all five people in the same order. The first element in every list is Madhu's sales for that year, the second is Kusum's, and so on.

index parameter: This assigns the sales person names as row labels. Without it, pandas would default to integer indices (0, 1, 2, 3, 4), losing the semantic meaning. …