import csv with different number of columns per row using Pandas

You can dynamically generate column names as simple counters (0, 1, 2, etc).

Dynamically generate column names

# Input
data_file = "smallsample.txt"

# Delimiter
data_file_delimiter = ','

# The max column count a line in the file could have
largest_column_count = 0

# Loop the data lines
with open(data_file, 'r') as temp_f:
    # Read the lines
    lines = temp_f.readlines()

    for l in lines:
        # Count the column count for the current line
        column_count = len(l.split(data_file_delimiter)) + 1
        
        # Set the new most column count
        largest_column_count = column_count if largest_column_count < column_count else largest_column_count

# Generate column names (will be 0, 1, 2, ..., largest_column_count - 1)
column_names = [i for i in range(0, largest_column_count)]

# Read csv
df = pandas.read_csv(data_file, header=None, delimiter=data_file_delimiter, names=column_names)
# print(df)

Missing values will be assigned to the columns which your CSV lines don't have a value for.

Supplying a list of columns names in the read_csv() should do the trick.

ex: names=['a', 'b', 'c', 'd', 'e']

https://github.com/pydata/pandas/issues/2981

Edit: if you don't want to supply column names then do what Nicholas suggested

Polished version of P.S. answer is as follows. It works. Remember we have inserted lot of missing values in the dataframe.

### Loop the data lines
with open("smallsample.txt", 'r') as temp_f:
    # get No of columns in each line
    col_count = [ len(l.split(",")) for l in temp_f.readlines() ]

### Generate column names  (names will be 0, 1, 2, ..., maximum columns - 1)
column_names = [i for i in range(0, max(col_count))]

### Read csv
df = pd.read_csv("smallsample.txt", header=None, delimiter=",", names=column_names)