Wide to Long Data Transformation

The problem at hand involves transforming a wide-format dataset into a long-format dataset using Python’s pandas library. The goal is to create a new dataset where each unique value of the Wavelength column has multiple rows, one for each reading.

Step 1: Identify Duplicate Readings

Upon examining the sample data, it becomes apparent that there are duplicate readings for certain wavelengths. Specifically, wavelength 796 appears twice in the second set of data.

Step 2: Handle Duplicate Readings

To handle these duplicates, we can introduce a new column to indicate which reading corresponds to each value. This is achieved by assigning a unique subreading number to each row within a group of duplicate readings.

Step 3: Transform Data from Wide to Long Format

Using the pd.wide_to_long function, we can transform the wide-format dataset into a long-format dataset. The resulting dataframe will have two columns: Wavelength and Data, representing the unique values of the original Wavelength1 and Data1 columns.

Step 4: Restore Duplicate Readings

To restore the duplicate readings, we can use the set_index method to set the Wavelength column as the index. Then, we can use the droplevel method to remove the top-level grouping (i.e., the original Wavelength1 and Data1 columns). Finally, we reset the index to create a new dataframe with the desired structure.

Step 5: Flatten Columns

To achieve the final output format, where each column represents a single reading for each wavelength, we can use the unstack method to flatten the columns. We then remove any duplicate subreading numbers by using the map function to join the original column names with an empty string.

Final Output

The resulting dataframe has the desired structure, where each row corresponds to a unique value of the Wavelength column and multiple rows correspond to the same wavelength.

df = pd.DataFrame({'Wavelength1': [800, 799, 798, 797],
                   'Data1': [0.1, 0.15, 0.133, 0.14],
                   'Wavelength2': [798, 797, 796, 796],
                   'Data2': [0.02, 0.03, 0.2, 0.052],
                   'Wavelength3': [798.5, 798.0, 797.5, 797.0],
                   'Data3': [0.6, 0.2, 0.4, 0.34]})

# wide to long
df2 = (
    pd.wide_to_long(df.reset_index(), ["Wavelength", "Data"], i="index", j="reading")
    .droplevel(0)
    .reset_index()
    .set_index(["Wavelength", "reading"])
)

# restore duplicate readings
df2 = df2.set_index(
    pd.Series(df2.groupby(level=[0, 1]).cumcount().values, name="subreading"),
    append=True,
).unstack("reading")

# flatten columns
df2.columns = ["".join(map(str, c)) for c in df2.columns]

print(df2)

Output:

                       Data
Wavelength subreading                     
796.0      0             NaN  0.200    NaN
           1             NaN  0.052    NaN
797.0      0           0.140  0.030   0.34
797.5      0             NaN    NaN   0.40
798.0      0           0.133  0.020   0.20
798.5      0             NaN    NaN   0.60
799.0      0           0.150    NaN    NaN
800.0      0           0.100    NaN    NaN

Note that this solution assumes the original dataset has a similar structure, with multiple readings for each wavelength in certain columns. The pd.wide_to_long function is used to transform the data, and subsequent steps are used to restore duplicate readings and flatten columns.

Last modified on 2024-07-02