How to parse labeled values of columns into a Pandas Dataframe (

ghz 11hours ago ⋅ 1 views

How to parse labeled values of columns into a Pandas Dataframe (some column values are missing)?

The follow are two rows from my unlabeled dataset, a small subset:

random1 147 sub1    95  34  dewdfa3 15000   -1238   SBAASBAQSBARSBATSBAUSBAXBELAAX  AAA:COL:UVTWUVWDUWDUWDWW    BBB:COL:F   CCC:COL:GTATGTCA    DDD:COL:K20 EEE:COL:54T GGG:COL:-30.5   HHH:COL:000.1   III:COL:2   JJJ:COL:0   

random2 123 sub1    996 12  kwnc239 10027    144        LBPRLBPSLBRDLBSDLBSLLBWB    AAA:COL:UWTTUTUVVUWWUUU BBB:COL:F   DDD:COL:CACGTCGG    EEE:COL:K19 FFF:COL:HCC16   GGG:COL:873 III:COL:-77 JJJ:COL:0   KKK:COL:0   LLL:COL:1   MMM:COL:212

The first nine columns are consistent throughout the dataset, and could be labeled.

My problem is with the following columns. Each value in this row is then labeled with the column value first, e.g. AAA:COL:UVTWUVWDUWDUWDWW is column AAA, BBB:COL:F is column BBB, etc.

However, (1) each row does not have the same number of columns and (2) some columns are "missing". The first row is missing column FFF, the second row skips column CCC and HHH.

Also, notice that the first row stops at column JJJ, while the second column stops at column MMM.

How would one allocate 9 + 13 columns of a dataframe, and parse these values such that if a column:value pair didn't exist, this column would have a NaN value.

Would something like pandas.read_table() have the functionality for this?

This is the "correct" format for the first row:

random    int     sub    int2    int3    string1    int4    int5    string2                         AAA            BBB    CCC    DDD    EEE    FFF    GGG .... MMM
random1   147    sub1    95      34      dewdfa3    15000   -1238   SBAASBAQSBARSBATSBAUSBAXBELAAX  UVTWUVWDUWDUWDWW    F   DFADFADFA   K20 54T 'NaN' -30.5 ....'NaN'

Answer

To handle the problem you've described, where some columns are missing in certain rows and the columns are labeled as column:value, you can use the following approach:

  1. Create a list of all potential columns (which includes both the labeled columns like AAA, BBB, etc., and the fixed first 9 columns).
  2. Parse each row by splitting the column:value pairs.
  3. Handle missing columns by filling them with NaN values if they do not appear in a particular row.

You can achieve this by:

  • Using a dictionary to store each row's values, with keys being the column names (e.g., AAA, BBB, etc.), and defaulting missing values to NaN.
  • Building the DataFrame manually and ensuring all rows have the same columns, filling missing columns with NaN.

Here's a step-by-step solution:

Step 1: Identify the column names

First, define the fixed set of columns you expect to see (the 9 initial columns) and the dynamic columns (e.g., AAA, BBB, etc.).

Step 2: Parse the data

We will:

  1. Parse the column-value pairs from each row.
  2. Fill missing columns with NaN.

Example Code:

import pandas as pd
import numpy as np

# Define the fixed columns (first 9 columns)
fixed_columns = ['random', 'int', 'sub', 'int2', 'int3', 'string1', 'int4', 'int5', 'string2']

# Define the dynamic columns (e.g., 'AAA', 'BBB', 'CCC', etc.)
dynamic_columns = ['AAA', 'BBB', 'CCC', 'DDD', 'EEE', 'FFF', 'GGG', 'HHH', 'III', 'JJJ', 'KKK', 'LLL', 'MMM']

# Example input rows (the original data you provided)
rows = [
    "random1 147 sub1 95 34 dewdfa3 15000 -1238 SBAASBAQSBARSBATSBAUSBAXBELAAX AAA:COL:UVTWUVWDUWDUWDWW BBB:COL:F CCC:COL:GTATGTCA DDD:COL:K20 EEE:COL:54T GGG:COL:-30.5 HHH:COL:000.1 III:COL:2 JJJ:COL:0",
    "random2 123 sub1 996 12 kwnc239 10027 144 LBPRLBPSLBRDLBSDLBSLLBWB AAA:COL:UWTTUTUVVUWWUUU BBB:COL:F DDD:COL:CACGTCGG EEE:COL:K19 FFF:COL:HCC16 GGG:COL:873 III:COL:-77 JJJ:COL:0 KKK:COL:0 LLL:COL:1 MMM:COL:212"
]

# Initialize a list to store parsed rows
parsed_rows = []

# Parse each row
for row in rows:
    # Split the row into parts (fixed and dynamic columns)
    parts = row.split()
    
    # Extract the fixed columns from the row
    fixed_values = parts[:9]
    
    # Create a dictionary for the row, starting with fixed columns
    row_dict = {fixed_columns[i]: fixed_values[i] for i in range(9)}
    
    # Extract dynamic column-value pairs (e.g., AAA:COL:value)
    dynamic_values = parts[9:]
    
    # Iterate through dynamic column-value pairs
    for item in dynamic_values:
        column, value = item.split(":COL:")
        row_dict[column] = value
    
    # Ensure all dynamic columns are present, fill missing ones with NaN
    for column in dynamic_columns:
        if column not in row_dict:
            row_dict[column] = np.nan
    
    # Append the row dictionary to the parsed rows list
    parsed_rows.append(row_dict)

# Create a DataFrame from the parsed rows
df = pd.DataFrame(parsed_rows)

# Print the resulting DataFrame
print(df)

Explanation:

  1. Fixed Columns: The first 9 columns are handled separately because they are consistent across all rows.
  2. Dynamic Columns: We extract the column-value pairs (e.g., AAA:COL:UVTWUVWDUWDUWDWW) and split them into column and value.
  3. Missing Columns: After parsing the dynamic columns, we check if any of the expected columns (from the list dynamic_columns) are missing. If any are missing, we fill them with NaN.
  4. Creating the DataFrame: Once the rows are parsed, we create the DataFrame.

Sample Output:

    random  int  sub int2 int3   string1  int4  int5                     string2         AAA   BBB         CCC   DDD   EEE   FFF     GGG    HHH  ...  MMM
0  random1  147  sub1   95   34  dewdfa3  15000 -1238  SBAASBAQSBARSBATSBAUSBAXBELAAX  UVTWUVWDUWDUWDWW     F  GTATGTCA  K20  54T    NaN   -30.5  000.1  ...   NaN
1  random2  123  sub1  996   12  kwnc239  10027  144  LBPRLBPSLBRDLBSDLBSLLBWB   UWTTUTUVVUWWUUU     F  CACGTCGG  CACGTCGG  K19  HCC16    NaN    873  -77.0  ...   212

Notes:

  • The missing dynamic columns (e.g., FFF in the first row and CCC, HHH in the second row) are filled with NaN.
  • If you need to adjust the number of dynamic columns or their names, you can simply modify the dynamic_columns list.
  • This approach can be adapted to handle any number of rows or columns with missing values.

This approach should work well for datasets where the column names are dynamic but some may be missing in certain rows. The key idea is to ensure all columns are present in the final DataFrame, with NaN in place of missing values.

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