Looking for adding a column to DATAFRAME including filtration of DATAFRAME

up vote
0
down vote

favorite

If I have a Dataframe is like this:

df =

       T1                        price

0 Today is Tuesday 60

1 Tomorrow is Wednesday 70

2 After Tomorrow is Thursday 80

3 The last day is Friday 90

If I want to get new Dataframe by adding column (independent variable) indicates the price when it is bigger than or equal to 80 (price >=80) using Python 3 "What should I do?"

This is an example to the Dataframe which I am looking for:

df =

       T1                        price         New

0 Today is Tuesday 60 Unknow

1 Tomorrow is Wednesday 70 Unknow

2 After Tomorrow is Thursday 80 80

3 The last day is Friday 90 90

I tried to write some code Using python, but I got only the rows where price is is bigger than or equal to 80 (price >=80) as following:

df =

       T1                         price

2 After Tomorrow is Thursday 80

3 The last day is Friday 90

I need really for your help to get another independent variable (new) including the price when it is bigger than or equal to 80 (price >=80) and give "Unknown" for the price is less than 80.

asked Nov 16 at 23:44

Elwakdy

add a comment |

up vote
0
down vote

favorite

If I have a Dataframe is like this:

df =

       T1                        price

0 Today is Tuesday 60

1 Tomorrow is Wednesday 70

2 After Tomorrow is Thursday 80

3 The last day is Friday 90

If I want to get new Dataframe by adding column (independent variable) indicates the price when it is bigger than or equal to 80 (price >=80) using Python 3 "What should I do?"

This is an example to the Dataframe which I am looking for:

df =

       T1                        price         New

0 Today is Tuesday 60 Unknow

1 Tomorrow is Wednesday 70 Unknow

2 After Tomorrow is Thursday 80 80

3 The last day is Friday 90 90

I tried to write some code Using python, but I got only the rows where price is is bigger than or equal to 80 (price >=80) as following:

df =

       T1                         price

2 After Tomorrow is Thursday 80

3 The last day is Friday 90

I need really for your help to get another independent variable (new) including the price when it is bigger than or equal to 80 (price >=80) and give "Unknown" for the price is less than 80.

asked Nov 16 at 23:44

Elwakdy

add a comment |

up vote
0
down vote

favorite

If I have a Dataframe is like this:

df =

       T1                        price

0 Today is Tuesday 60

1 Tomorrow is Wednesday 70

2 After Tomorrow is Thursday 80

3 The last day is Friday 90

If I want to get new Dataframe by adding column (independent variable) indicates the price when it is bigger than or equal to 80 (price >=80) using Python 3 "What should I do?"

This is an example to the Dataframe which I am looking for:

df =

       T1                        price         New

0 Today is Tuesday 60 Unknow

1 Tomorrow is Wednesday 70 Unknow

2 After Tomorrow is Thursday 80 80

3 The last day is Friday 90 90

I tried to write some code Using python, but I got only the rows where price is is bigger than or equal to 80 (price >=80) as following:

df =

       T1                         price

2 After Tomorrow is Thursday 80

3 The last day is Friday 90

I need really for your help to get another independent variable (new) including the price when it is bigger than or equal to 80 (price >=80) and give "Unknown" for the price is less than 80.

asked Nov 16 at 23:44

Elwakdy

If I have a Dataframe is like this:

df =

       T1                        price

0 Today is Tuesday 60

1 Tomorrow is Wednesday 70

2 After Tomorrow is Thursday 80

3 The last day is Friday 90

If I want to get new Dataframe by adding column (independent variable) indicates the price when it is bigger than or equal to 80 (price >=80) using Python 3 "What should I do?"

This is an example to the Dataframe which I am looking for:

df =

       T1                        price         New

0 Today is Tuesday 60 Unknow

1 Tomorrow is Wednesday 70 Unknow

2 After Tomorrow is Thursday 80 80

3 The last day is Friday 90 90

I tried to write some code Using python, but I got only the rows where price is is bigger than or equal to 80 (price >=80) as following:

df =

       T1                         price

2 After Tomorrow is Thursday 80

3 The last day is Friday 90

I need really for your help to get another independent variable (new) including the price when it is bigger than or equal to 80 (price >=80) and give "Unknown" for the price is less than 80.

python dataframe max

asked Nov 16 at 23:44

Elwakdy

asked Nov 16 at 23:44

Elwakdy

asked Nov 16 at 23:44

Elwakdy

asked Nov 16 at 23:44

Elwakdy

asked Nov 16 at 23:44

Elwakdy

add a comment |

1 Answer
1

active

oldest

votes

up vote
0
down vote

accepted

Try: df['new'] = df['price'].apply(lambda x: x if x >= 80 else 'unknown')

It is hard to read from your unformatted tables but I believe this is what you want.

answered Nov 17 at 0:09

BernardL

1,567828

Thank you so much, BernardL. If I want to change all numerical values (numbers are unlimited) to 1 and "Unknown" to 0 in "new" column (independent variable) "What should I do?"
– Elwakdy
2 days ago

I found the solution df.loc[df.new == "unknown",'new'] = 0; df df.loc[df.new > 0,'new'] = 1; df
– Elwakdy
2 days ago

Or you can do this: df['price'].apply(lambda x: 1 if x >= 80 else 0), you can see the change made here and it basically means that if the value of the column is more than 80, assign 1 else assign 0. Hope it helped.
– BernardL
2 days ago

Thank you so much, BernardL. I tested the line code and see the conversion to 1 and 0 happened in another Dataframe "What should I do to convert the values to 1 and "unknown" to 0 in same the DataFrame?
– Elwakdy
2 days ago

Same column you mean? You need to make sure you are assigning the new column to the same dataframe.
– BernardL
2 days ago

|
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1 Answer
1

active

oldest

votes

1 Answer
1

active

oldest

votes

up vote
0
down vote

accepted

Try: df['new'] = df['price'].apply(lambda x: x if x >= 80 else 'unknown')

It is hard to read from your unformatted tables but I believe this is what you want.

answered Nov 17 at 0:09

BernardL

1,567828

Thank you so much, BernardL. If I want to change all numerical values (numbers are unlimited) to 1 and "Unknown" to 0 in "new" column (independent variable) "What should I do?"
– Elwakdy
2 days ago

I found the solution df.loc[df.new == "unknown",'new'] = 0; df df.loc[df.new > 0,'new'] = 1; df
– Elwakdy
2 days ago

Or you can do this: df['price'].apply(lambda x: 1 if x >= 80 else 0), you can see the change made here and it basically means that if the value of the column is more than 80, assign 1 else assign 0. Hope it helped.
– BernardL
2 days ago

Thank you so much, BernardL. I tested the line code and see the conversion to 1 and 0 happened in another Dataframe "What should I do to convert the values to 1 and "unknown" to 0 in same the DataFrame?
– Elwakdy
2 days ago

Same column you mean? You need to make sure you are assigning the new column to the same dataframe.
– BernardL
2 days ago

|
show 1 more comment

up vote
0
down vote

accepted

Try: df['new'] = df['price'].apply(lambda x: x if x >= 80 else 'unknown')

It is hard to read from your unformatted tables but I believe this is what you want.

answered Nov 17 at 0:09

BernardL

1,567828

Thank you so much, BernardL. If I want to change all numerical values (numbers are unlimited) to 1 and "Unknown" to 0 in "new" column (independent variable) "What should I do?"
– Elwakdy
2 days ago

I found the solution df.loc[df.new == "unknown",'new'] = 0; df df.loc[df.new > 0,'new'] = 1; df
– Elwakdy
2 days ago

Or you can do this: df['price'].apply(lambda x: 1 if x >= 80 else 0), you can see the change made here and it basically means that if the value of the column is more than 80, assign 1 else assign 0. Hope it helped.
– BernardL
2 days ago

Thank you so much, BernardL. I tested the line code and see the conversion to 1 and 0 happened in another Dataframe "What should I do to convert the values to 1 and "unknown" to 0 in same the DataFrame?
– Elwakdy
2 days ago

Same column you mean? You need to make sure you are assigning the new column to the same dataframe.
– BernardL
2 days ago

|
show 1 more comment

up vote
0
down vote

accepted

Try: df['new'] = df['price'].apply(lambda x: x if x >= 80 else 'unknown')

It is hard to read from your unformatted tables but I believe this is what you want.

answered Nov 17 at 0:09

BernardL

1,567828

Try: df['new'] = df['price'].apply(lambda x: x if x >= 80 else 'unknown')

It is hard to read from your unformatted tables but I believe this is what you want.

answered Nov 17 at 0:09

BernardL

1,567828

answered Nov 17 at 0:09

BernardL

1,567828

answered Nov 17 at 0:09

BernardL

1,567828

answered Nov 17 at 0:09

BernardL

1,567828

Thank you so much, BernardL. If I want to change all numerical values (numbers are unlimited) to 1 and "Unknown" to 0 in "new" column (independent variable) "What should I do?"
– Elwakdy
2 days ago

I found the solution df.loc[df.new == "unknown",'new'] = 0; df df.loc[df.new > 0,'new'] = 1; df
– Elwakdy
2 days ago

Or you can do this: df['price'].apply(lambda x: 1 if x >= 80 else 0), you can see the change made here and it basically means that if the value of the column is more than 80, assign 1 else assign 0. Hope it helped.
– BernardL
2 days ago

Thank you so much, BernardL. I tested the line code and see the conversion to 1 and 0 happened in another Dataframe "What should I do to convert the values to 1 and "unknown" to 0 in same the DataFrame?
– Elwakdy
2 days ago

Same column you mean? You need to make sure you are assigning the new column to the same dataframe.
– BernardL
2 days ago

|
show 1 more comment

Thank you so much, BernardL. If I want to change all numerical values (numbers are unlimited) to 1 and "Unknown" to 0 in "new" column (independent variable) "What should I do?"
– Elwakdy
2 days ago

I found the solution df.loc[df.new == "unknown",'new'] = 0; df df.loc[df.new > 0,'new'] = 1; df
– Elwakdy
2 days ago

Or you can do this: df['price'].apply(lambda x: 1 if x >= 80 else 0), you can see the change made here and it basically means that if the value of the column is more than 80, assign 1 else assign 0. Hope it helped.
– BernardL
2 days ago

Thank you so much, BernardL. I tested the line code and see the conversion to 1 and 0 happened in another Dataframe "What should I do to convert the values to 1 and "unknown" to 0 in same the DataFrame?
– Elwakdy
2 days ago

Same column you mean? You need to make sure you are assigning the new column to the same dataframe.
– BernardL
2 days ago

Thank you so much, BernardL. If I want to change all numerical values (numbers are unlimited) to 1 and "Unknown" to 0 in "new" column (independent variable) "What should I do?"
– Elwakdy
2 days ago

I found the solution df.loc[df.new == "unknown",'new'] = 0; df df.loc[df.new > 0,'new'] = 1; df
– Elwakdy
2 days ago

Or you can do this: df['price'].apply(lambda x: 1 if x >= 80 else 0), you can see the change made here and it basically means that if the value of the column is more than 80, assign 1 else assign 0. Hope it helped.
– BernardL
2 days ago

Thank you so much, BernardL. I tested the line code and see the conversion to 1 and 0 happened in another Dataframe "What should I do to convert the values to 1 and "unknown" to 0 in same the DataFrame?
– Elwakdy
2 days ago

Same column you mean? You need to make sure you are assigning the new column to the same dataframe.
– BernardL
2 days ago

|
show 1 more comment

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