Converting dataset to Reward/Miss dataset in Python
2
I want to convert the following dataset (CSV) using Pandas and NumPy in Python:
Table 1 (csv)
Ads, Impressions, Clicks
Ad_1, 11, 1
Ad_2, 10, 2
to
Table 2 (csv)
Ad_1, Ad_2
0, 0
0, 0
0, 0
0, 1
0, 0
1, 0
0, 0
0, 0
0, 1
0, 0
0
Table 2 has basically impressions as total number of rows with random insertion of 1's (count = Clicks).
The converted table is to run CTR optimization on 2 Ad sets using Upper Confidence Bound algorithm using machine learning. Kindly help how to convert Table 1 to Table 2.
Thanks!
python pandas numpy
edited Dec 28 '18 at 16:59
desertnaut
16.8k63566
asked Dec 28 '18 at 13:03
Vaibhav MagonVaibhav Magon
1,0501926
add a comment |
2
I want to convert the following dataset (CSV) using Pandas and NumPy in Python:
Table 1 (csv)
Ads, Impressions, Clicks
Ad_1, 11, 1
Ad_2, 10, 2
to
Table 2 (csv)
Ad_1, Ad_2
0, 0
0, 0
0, 0
0, 1
0, 0
1, 0
0, 0
0, 0
0, 1
0, 0
0
Table 2 has basically impressions as total number of rows with random insertion of 1's (count = Clicks).
The converted table is to run CTR optimization on 2 Ad sets using Upper Confidence Bound algorithm using machine learning. Kindly help how to convert Table 1 to Table 2.
Thanks!
python pandas numpy
edited Dec 28 '18 at 16:59
desertnaut
16.8k63566
asked Dec 28 '18 at 13:03
Vaibhav MagonVaibhav Magon
1,0501926
Question has nothing to do withmachine-learning- kindly do not spam the tag (removed).
– desertnaut
Dec 28 '18 at 16:59
It was just to call in more help!
– Vaibhav Magon
Dec 29 '18 at 10:27
That's not a legitimate use of tags (let alone that the python/pandas/numpy community is already big enough) - kindly refrain from the practice next time
– desertnaut
Dec 29 '18 at 12:23
add a comment |
2
2
2
I want to convert the following dataset (CSV) using Pandas and NumPy in Python:
Table 1 (csv)
Ads, Impressions, Clicks
Ad_1, 11, 1
Ad_2, 10, 2
to
Table 2 (csv)
Ad_1, Ad_2
0, 0
0, 0
0, 0
0, 1
0, 0
1, 0
0, 0
0, 0
0, 1
0, 0
0
Table 2 has basically impressions as total number of rows with random insertion of 1's (count = Clicks).
The converted table is to run CTR optimization on 2 Ad sets using Upper Confidence Bound algorithm using machine learning. Kindly help how to convert Table 1 to Table 2.
Thanks!
python pandas numpy
edited Dec 28 '18 at 16:59
desertnaut
16.8k63566
asked Dec 28 '18 at 13:03
Vaibhav MagonVaibhav Magon
1,0501926
I want to convert the following dataset (CSV) using Pandas and NumPy in Python:
Table 1 (csv)
Ads, Impressions, Clicks
Ad_1, 11, 1
Ad_2, 10, 2
to
Table 2 (csv)
Ad_1, Ad_2
0, 0
0, 0
0, 0
0, 1
0, 0
1, 0
0, 0
0, 0
0, 1
0, 0
0
Table 2 has basically impressions as total number of rows with random insertion of 1's (count = Clicks).
The converted table is to run CTR optimization on 2 Ad sets using Upper Confidence Bound algorithm using machine learning. Kindly help how to convert Table 1 to Table 2.
Thanks!
python pandas numpy
python pandas numpy
edited Dec 28 '18 at 16:59
desertnaut
16.8k63566
asked Dec 28 '18 at 13:03
Vaibhav MagonVaibhav Magon
1,0501926
edited Dec 28 '18 at 16:59
desertnaut
16.8k63566
asked Dec 28 '18 at 13:03
Vaibhav MagonVaibhav Magon
1,0501926
edited Dec 28 '18 at 16:59
desertnaut
16.8k63566
edited Dec 28 '18 at 16:59
desertnaut
16.8k63566
edited Dec 28 '18 at 16:59
desertnaut
16.8k63566
16.8k63566
asked Dec 28 '18 at 13:03
Vaibhav MagonVaibhav Magon
1,0501926
asked Dec 28 '18 at 13:03
Vaibhav MagonVaibhav Magon
1,0501926
asked Dec 28 '18 at 13:03
Vaibhav MagonVaibhav Magon
1,0501926
1,0501926
Question has nothing to do withmachine-learning- kindly do not spam the tag (removed).
– desertnaut
Dec 28 '18 at 16:59
It was just to call in more help!
– Vaibhav Magon
Dec 29 '18 at 10:27
That's not a legitimate use of tags (let alone that the python/pandas/numpy community is already big enough) - kindly refrain from the practice next time
– desertnaut
Dec 29 '18 at 12:23
add a comment |
Question has nothing to do withmachine-learning- kindly do not spam the tag (removed).
– desertnaut
Dec 28 '18 at 16:59
It was just to call in more help!
– Vaibhav Magon
Dec 29 '18 at 10:27
That's not a legitimate use of tags (let alone that the python/pandas/numpy community is already big enough) - kindly refrain from the practice next time
– desertnaut
Dec 29 '18 at 12:23
Question has nothing to do with
machine-learning - kindly do not spam the tag (removed).– desertnaut
Dec 28 '18 at 16:59
Question has nothing to do with
machine-learning - kindly do not spam the tag (removed).– desertnaut
Dec 28 '18 at 16:59
It was just to call in more help!
– Vaibhav Magon
Dec 29 '18 at 10:27
It was just to call in more help!
– Vaibhav Magon
Dec 29 '18 at 10:27
That's not a legitimate use of tags (let alone that the python/pandas/numpy community is already big enough) - kindly refrain from the practice next time
– desertnaut
Dec 29 '18 at 12:23
That's not a legitimate use of tags (let alone that the python/pandas/numpy community is already big enough) - kindly refrain from the practice next time
– desertnaut
Dec 29 '18 at 12:23
add a comment |
1 Answer
1
active
oldest
votes
1
I think this should do the trick:
import pandas as pd
import numpy as np
from io import StringIO
TESTDATA = StringIO("""Ads,Impressions,Clicks
Ad_1, 11, 1
Ad_2, 10, 2
""")
table_1 = pd.read_csv(TESTDATA, sep=",")
def convert(row):
clicks_to_generate = row['Clicks']
array_len = row['Impressions']
ad = np.zeros(array_len)
ad[:clicks_to_generate] = 1
np.random.shuffle(ad) # you want it random
return ad
ads = table_1.apply(convert, axis=1)
series_list = [pd.Series(ad) for ad in ads]
table_2 = pd.DataFrame(series_list).T
table_2 = table_2.add_prefix('Ad_')
print(table_2)
Ad_0 Ad_1
0 0.0 0.0
1 1.0 0.0
2 0.0 1.0
3 0.0 1.0
4 0.0 0.0
5 0.0 0.0
6 0.0 0.0
7 0.0 0.0
8 0.0 0.0
9 0.0 0.0
10 0.0 NaN
table_2.to_csv('table_2.csv', index=False)
answered Dec 28 '18 at 15:29
Lukasz TracewskiLukasz Tracewski
2,1211020
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
1
I think this should do the trick:
import pandas as pd
import numpy as np
from io import StringIO
TESTDATA = StringIO("""Ads,Impressions,Clicks
Ad_1, 11, 1
Ad_2, 10, 2
""")
table_1 = pd.read_csv(TESTDATA, sep=",")
def convert(row):
clicks_to_generate = row['Clicks']
array_len = row['Impressions']
ad = np.zeros(array_len)
ad[:clicks_to_generate] = 1
np.random.shuffle(ad) # you want it random
return ad
ads = table_1.apply(convert, axis=1)
series_list = [pd.Series(ad) for ad in ads]
table_2 = pd.DataFrame(series_list).T
table_2 = table_2.add_prefix('Ad_')
print(table_2)
Ad_0 Ad_1
0 0.0 0.0
1 1.0 0.0
2 0.0 1.0
3 0.0 1.0
4 0.0 0.0
5 0.0 0.0
6 0.0 0.0
7 0.0 0.0
8 0.0 0.0
9 0.0 0.0
10 0.0 NaN
table_2.to_csv('table_2.csv', index=False)
answered Dec 28 '18 at 15:29
Lukasz TracewskiLukasz Tracewski
2,1211020
add a comment |
1
I think this should do the trick:
import pandas as pd
import numpy as np
from io import StringIO
TESTDATA = StringIO("""Ads,Impressions,Clicks
Ad_1, 11, 1
Ad_2, 10, 2
""")
table_1 = pd.read_csv(TESTDATA, sep=",")
def convert(row):
clicks_to_generate = row['Clicks']
array_len = row['Impressions']
ad = np.zeros(array_len)
ad[:clicks_to_generate] = 1
np.random.shuffle(ad) # you want it random
return ad
ads = table_1.apply(convert, axis=1)
series_list = [pd.Series(ad) for ad in ads]
table_2 = pd.DataFrame(series_list).T
table_2 = table_2.add_prefix('Ad_')
print(table_2)
Ad_0 Ad_1
0 0.0 0.0
1 1.0 0.0
2 0.0 1.0
3 0.0 1.0
4 0.0 0.0
5 0.0 0.0
6 0.0 0.0
7 0.0 0.0
8 0.0 0.0
9 0.0 0.0
10 0.0 NaN
table_2.to_csv('table_2.csv', index=False)
answered Dec 28 '18 at 15:29
Lukasz TracewskiLukasz Tracewski
2,1211020
add a comment |
1
1
1
I think this should do the trick:
import pandas as pd
import numpy as np
from io import StringIO
TESTDATA = StringIO("""Ads,Impressions,Clicks
Ad_1, 11, 1
Ad_2, 10, 2
""")
table_1 = pd.read_csv(TESTDATA, sep=",")
def convert(row):
clicks_to_generate = row['Clicks']
array_len = row['Impressions']
ad = np.zeros(array_len)
ad[:clicks_to_generate] = 1
np.random.shuffle(ad) # you want it random
return ad
ads = table_1.apply(convert, axis=1)
series_list = [pd.Series(ad) for ad in ads]
table_2 = pd.DataFrame(series_list).T
table_2 = table_2.add_prefix('Ad_')
print(table_2)
Ad_0 Ad_1
0 0.0 0.0
1 1.0 0.0
2 0.0 1.0
3 0.0 1.0
4 0.0 0.0
5 0.0 0.0
6 0.0 0.0
7 0.0 0.0
8 0.0 0.0
9 0.0 0.0
10 0.0 NaN
table_2.to_csv('table_2.csv', index=False)
answered Dec 28 '18 at 15:29
Lukasz TracewskiLukasz Tracewski
2,1211020
I think this should do the trick:
import pandas as pd
import numpy as np
from io import StringIO
TESTDATA = StringIO("""Ads,Impressions,Clicks
Ad_1, 11, 1
Ad_2, 10, 2
""")
table_1 = pd.read_csv(TESTDATA, sep=",")
def convert(row):
clicks_to_generate = row['Clicks']
array_len = row['Impressions']
ad = np.zeros(array_len)
ad[:clicks_to_generate] = 1
np.random.shuffle(ad) # you want it random
return ad
ads = table_1.apply(convert, axis=1)
series_list = [pd.Series(ad) for ad in ads]
table_2 = pd.DataFrame(series_list).T
table_2 = table_2.add_prefix('Ad_')
print(table_2)
Ad_0 Ad_1
0 0.0 0.0
1 1.0 0.0
2 0.0 1.0
3 0.0 1.0
4 0.0 0.0
5 0.0 0.0
6 0.0 0.0
7 0.0 0.0
8 0.0 0.0
9 0.0 0.0
10 0.0 NaN
table_2.to_csv('table_2.csv', index=False)
answered Dec 28 '18 at 15:29
Lukasz TracewskiLukasz Tracewski
2,1211020
edited Dec 28 '18 at 15:35
answered Dec 28 '18 at 15:29
Lukasz TracewskiLukasz Tracewski
2,1211020
answered Dec 28 '18 at 15:29
Lukasz TracewskiLukasz Tracewski
2,1211020
answered Dec 28 '18 at 15:29
Lukasz TracewskiLukasz Tracewski
2,1211020
2,1211020
add a comment |
add a comment |
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Question has nothing to do with
machine-learning- kindly do not spam the tag (removed).– desertnaut
Dec 28 '18 at 16:59
It was just to call in more help!
– Vaibhav Magon
Dec 29 '18 at 10:27
That's not a legitimate use of tags (let alone that the python/pandas/numpy community is already big enough) - kindly refrain from the practice next time
– desertnaut
Dec 29 '18 at 12:23