Split data into 3 column dataframe

Multiple approaches can be taken here depending on the input file type and format. If the file is a valid string path, try these approaches (more here):

import pandas as pd
# approach 1
df = pd.read_fwf('inputfile.txt')

# approach 2
df = pd.read_csv("inputfile.txt", sep = "\t") # check the delimiter

# then select the columns you want
df_subset = df[['Server', 'Date', 'Address']]

Full solution:

import pandas as pd

# read in text file
df = pd.read_csv("test_input.txt", sep=" ", error_bad_lines=False)

# convert df to string
df = df.astype(str)

# get num rows
num_rows = df.shape[0]

# get IP from index, then reset index
df['IP'] = df.index

# reset index to proper index
new_index = pd.Series(list(range(num_rows)))
df = df.set_index([new_index])

# rename columns and drop old cols
df = df.rename(columns={'Server': 'Date', 'IP': "Server"})

# create Date col, drop old col
df['Date'] = df.Date.str.cat(df['Unnamed: 1'])
df = df.drop(["Unnamed: 1"], axis=1)

# Create address col, drop old col
df['Address'] = df['Unnamed: 2'] + df['Unnamed: 3'] + df['Unnamed: 4']
df = df.drop(["Unnamed: 2","Unnamed: 3","Unnamed: 4"], axis=1)

# Strip brackets, other chars
df['Date'] = df['Date'].str.strip("[]")
df['Server'] = df["Server"].astype(str)
df['Server'] = df['Server'].str.strip("()-'', '-',")

Returns: