How can I convert array to dataframe?

Question

I have a dataframe:

ID
239200202
14700993
1153709258720067584

And have a output whether the id is a bot or not in an array form [1,1,0] How can I combine it into one dataframe like:

ID	Bot
239200202	bot
14700993	bot
1153709258720067584	Not bot

I tried this code, but it didn't work:

test = pd.read_csv('./user_data/user_lookup/dataset/test_dataframe.csv', index_col=1)
df = pd.DataFrame(columns=['UserID','Bot/Not'])
for index,row in test.iterrows():
   if test[index] == 1:
      df.loc[index,['UserID']] = test['User ID']
      df.loc[index,['Bot/Not']] = 'Bot'
   if test[index] == 0:
      df.loc[index, ['UserID']] = test['User ID']
      df.loc[index, ['Bot/Not']] = 'Not-Bot'
print(df)

It would be great if someone can help me out. Thank you

yes test_dataframe.csv only has ids. I dropped the rest of the columns because I don't need it. — Chirag atha
– Chirag atha, Commented Jun 3, 2021 at 6:15
array is an output from: pred_logreg_test = logreg.predict(test_scaled) I am predicting the output array whether the id is a bot or not — Chirag atha
– Chirag atha, Commented Jun 3, 2021 at 6:16
you can use pandas assign, for you array/list you can do a list comprehension arr =[1,1,0] arr = ['Bot' if x==1 else 'Not-Bot' for x in arr] — Shijith
– Shijith, Commented Jun 3, 2021 at 6:17

Prakash Dahal · Accepted Answer · 2021-06-03 06:17:15Z

1

According to the hints that you have given in the question,

You can add the column name Bot to the test dataframe as follow:

new_pred = ['bot' if x==1 else 'Not bot' for x in pred_logreg_test]
test['Bot'] = list(new_pred)

answered Jun 3, 2021 at 6:17

Prakash Dahal

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Comments

Vivs · Accepted Answer · 2021-06-03 06:21:49Z

1

Here is the solution to the above problem

array = [1,1,0]
df['BOT']=df.loc[df['ID'].isin(array)]

answered Jun 3, 2021 at 6:21

Vivs

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masasa · Accepted Answer · 2021-06-03 06:23:19Z

1

it's best to use here with pd.concat , to merge this 2 df into one

also, try to avoid iterrows at any cost while working with DataFrames, its substantially slower

example:

import pandas as pd
import numpy as np

df = pd.DataFrame({'ID': [100, 101, 102]})
bot_not_bot = np.array([1,0,1])
df = pd.concat([df, pd.DataFrame({'bot/not bot': bot_not_bot})], axis=1)

instead of using iterrows which is slower, use apply for faster results on larger scale DataFrames

df['bot/not bot'] = df['bot/not bot'].apply(lambda x: 'Bot' if x else 'Not Bot')

This is the correct way to use Dataframes, avoid iterrows

answered Jun 3, 2021 at 6:23

masasa

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Comments

Daniel · Accepted Answer · 2021-06-03 06:31:50Z

0

Use indexing into an array:

df = pd.DataFrame({'UserID': [239200202, 14700993, 1153709258720067584]})
is_bot = np.array([1,1,0])
df['Bot'] = np.array(['not bot', 'bot'])[is_bot]

answered Jun 3, 2021 at 6:31

Daniel

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