Mfcttrf

How can religions be structured in ways that allow inter-faith councils to work?

Is there a way to know the composition of a Team GO Rocket before going into the fight?

World of (nearly) identical snowflakes

Do the books ever say oliphaunts aren’t elephants?

How did the Axis intend to hold the Caucasus?

reconstruction filter - How does it actually work?

Assuring luggage isn't lost with short layover

Does Wolfram Mathworld make a mistake describing a discrete probability distribution with a probability density function?

Move the outer key inward in an association

How does one get an animal off of the Altar surreptitiously?

How likely is fragmentation on a table with 40000 products likely to affect performance

Can I change the license of a forked project to the MIT if the license of the parent project has changed from the GPL to the MIT?

Are there any unpublished Iain M. Banks short stories?

Why not notify faculty candidates of the position being filled?

Why force the nose of 737 Max down in the first place?

How to find the mass density function of a solid given its total mass, its volume and the position of its center of mass?

Why is it "on the inside" and not "in the inside"?

Is this photo showing a woman standing in the nude before teenagers real?

Polyhedra, Polyhedron, Polytopes and Polygon

Composing fill in the blanks

Why did House of Representatives need to condemn Trumps Tweets?

An easy decision when to use a spline or a polynomial

Are the named pipe created by `mknod` and the FIFO created by `mkfifo` equivalent?

Summoning A Technology Based Demon

Incrementing add under condition in pandas

Does Python have a ternary conditional operator?Add new keys to a dictionary?Converting a Pandas GroupBy output from Series to DataFrameRenaming columns in pandasAdding new column to existing DataFrame in Python pandasHow can I replace all the NaN values with Zero's in a column of a pandas dataframe“Large data” work flows using pandasChange data type of columns in PandasSelect rows from a DataFrame based on values in a column in pandasWhy is “1000000000000000 in range(1000000000000001)” so fast in Python 3?

.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;

6

For the following pandas dataframe

 servo_in_position second_servo_in_position Expected output
0 0 1 0
1 0 1 0
2 1 2 1
3 0 3 0
4 1 4 2
5 1 4 2
6 0 5 0
7 0 5 0
8 1 6 3
9 0 7 0
10 1 8 4
11 0 9 0
12 1 10 5
13 1 10 5
14 1 10 5
15 0 11 0
16 0 11 0
17 0 11 0
18 1 12 6
19 1 12 6
20 0 13 0
21 0 13 0
22 0 13 0

I want to increment the column "Expected output" only if "servo_in_position" changes from 0 to 1. I want also to assume "Expected output" to be 0 (null) if "servo_in_position" equals to 0.

I tried

input_data['second_servo_in_position']=(input_data.servo_in_position.diff()!=0).cumsum()

but it gives output as in "second_servo_in_position" column, which is not what I wanted.

After that I would like to group and calculate mean using:

print("Mean=nn",input_data.groupby('second_servo_in_position').mean())

python pandas

share|improve this question

edited 8 hours ago

Scott Boston

64.7k77 gold badges3838 silver badges6262 bronze badges

asked 9 hours ago

TomaszTomasz

6388 bronze badges

add a comment | 

6

For the following pandas dataframe

 servo_in_position second_servo_in_position Expected output
0 0 1 0
1 0 1 0
2 1 2 1
3 0 3 0
4 1 4 2
5 1 4 2
6 0 5 0
7 0 5 0
8 1 6 3
9 0 7 0
10 1 8 4
11 0 9 0
12 1 10 5
13 1 10 5
14 1 10 5
15 0 11 0
16 0 11 0
17 0 11 0
18 1 12 6
19 1 12 6
20 0 13 0
21 0 13 0
22 0 13 0

I want to increment the column "Expected output" only if "servo_in_position" changes from 0 to 1. I want also to assume "Expected output" to be 0 (null) if "servo_in_position" equals to 0.

I tried

input_data['second_servo_in_position']=(input_data.servo_in_position.diff()!=0).cumsum()

but it gives output as in "second_servo_in_position" column, which is not what I wanted.

After that I would like to group and calculate mean using:

print("Mean=nn",input_data.groupby('second_servo_in_position').mean())

python pandas

share|improve this question

edited 8 hours ago

Scott Boston

64.7k77 gold badges3838 silver badges6262 bronze badges

asked 9 hours ago

TomaszTomasz

6388 bronze badges

add a comment | 

For the following pandas dataframe

 servo_in_position second_servo_in_position Expected output
0 0 1 0
1 0 1 0
2 1 2 1
3 0 3 0
4 1 4 2
5 1 4 2
6 0 5 0
7 0 5 0
8 1 6 3
9 0 7 0
10 1 8 4
11 0 9 0
12 1 10 5
13 1 10 5
14 1 10 5
15 0 11 0
16 0 11 0
17 0 11 0
18 1 12 6
19 1 12 6
20 0 13 0
21 0 13 0
22 0 13 0

I want to increment the column "Expected output" only if "servo_in_position" changes from 0 to 1. I want also to assume "Expected output" to be 0 (null) if "servo_in_position" equals to 0.

I tried

input_data['second_servo_in_position']=(input_data.servo_in_position.diff()!=0).cumsum()

but it gives output as in "second_servo_in_position" column, which is not what I wanted.

After that I would like to group and calculate mean using:

print("Mean=nn",input_data.groupby('second_servo_in_position').mean())

python pandas

share|improve this question

edited 8 hours ago

Scott Boston

64.7k77 gold badges3838 silver badges6262 bronze badges

asked 9 hours ago

TomaszTomasz

6388 bronze badges

 servo_in_position second_servo_in_position Expected output
0 0 1 0
1 0 1 0
2 1 2 1
3 0 3 0
4 1 4 2
5 1 4 2
6 0 5 0
7 0 5 0
8 1 6 3
9 0 7 0
10 1 8 4
11 0 9 0
12 1 10 5
13 1 10 5
14 1 10 5
15 0 11 0
16 0 11 0
17 0 11 0
18 1 12 6
19 1 12 6
20 0 13 0
21 0 13 0
22 0 13 0

input_data['second_servo_in_position']=(input_data.servo_in_position.diff()!=0).cumsum()

print("Mean=nn",input_data.groupby('second_servo_in_position').mean())

share|improve this question

edited 8 hours ago

Scott Boston

64.7k77 gold badges3838 silver badges6262 bronze badges

asked 9 hours ago

TomaszTomasz

6388 bronze badges

share|improve this question

edited 8 hours ago

Scott Boston

64.7k77 gold badges3838 silver badges6262 bronze badges

asked 9 hours ago

TomaszTomasz

6388 bronze badges

edited 8 hours ago

Scott Boston

64.7k77 gold badges3838 silver badges6262 bronze badges

edited 8 hours ago

Scott Boston

64.7k77 gold badges3838 silver badges6262 bronze badges

asked 9 hours ago

TomaszTomasz

6388 bronze badges

asked 9 hours ago

TomaszTomasz

6388 bronze badges

5 Answers
5

active

oldest

votes

7

Use cumsum and mask:

df['E_output'] = df['servo_in_position'].diff().eq(1).cumsum()
 .mask(df['servo_in_position'] == 0, 0)

Output:

 servo_in_position second_servo_in_position Expected output E_output
0 0 1 0 0
1 0 1 0 0
2 1 2 1 1
3 0 3 0 0
4 1 4 2 2
5 1 4 2 2
6 0 5 0 0
7 0 5 0 0
8 1 6 3 3
9 0 7 0 0
10 1 8 4 4
11 0 9 0 0
12 1 10 5 5
13 1 10 5 5
14 1 10 5 5
15 0 11 0 0
16 0 11 0 0
17 0 11 0 0
18 1 12 6 6
19 1 12 6 6
20 0 13 0 0
21 0 13 0 0
22 0 13 0 0

share|improve this answer

answered 9 hours ago

Scott BostonScott Boston

64.7k77 gold badges3838 silver badges6262 bronze badges

add a comment | 

6

Try np.where:

df['Expected_output'] = np.where(df.servo_in_position.eq(1),
 df.servo_in_position.diff().eq(1).cumsum(),
 0)

share|improve this answer

answered 9 hours ago

Quang HoangQuang Hoang

14.4k22 gold badges1414 silver badges2424 bronze badges

add a comment | 

6

Using cumsum and arithmetic.

---

u = df['servo_in_position']

(u.eq(1) & u.shift().ne(1)).cumsum() * u

0 0
1 0
2 1
3 0
4 2
5 2
6 0
7 0
8 3
9 0
10 4
11 0
12 5
13 5
14 5
15 0
16 0
17 0
18 6
19 6
20 0
21 0
22 0
Name: servo_in_position, dtype: int64

share|improve this answer

answered 9 hours ago

user3483203user3483203

36.7k88 gold badges3131 silver badges6060 bronze badges

add a comment | 

5

That is cumsum and mul

df.servo_in_position.diff().eq(1).cumsum().mul(df.servo_in_position.eq(1),axis=0)

share|improve this answer

answered 9 hours ago

WeNYoBenWeNYoBen

150k88 gold badges5353 silver badges8484 bronze badges

add a comment | 

3

Fast with Numba

from numba import njit

@njit
def f(u):
 out = np.zeros(len(u), np.int64)
 a = out[0] = u[0]
 for i in range(1, len(u)):
 if u[i] == 1:
 if u[i - 1] == 0:
 a += 1
 out[i] = a
 return out

f(df.servo_in_position.to_numpy())

array([0, 0, 1, 0, 2, 2, 0, 0, 3, 0, 4, 0, 5, 5, 5, 0, 0, 0, 6, 6, 0, 0, 0])

share|improve this answer

answered 7 hours ago

piRSquaredpiRSquared

174k2626 gold badges188188 silver badges343343 bronze badges

add a comment | 

Your Answer

StackExchange.ifUsing("editor", function ()
StackExchange.using("externalEditor", function ()
StackExchange.using("snippets", function ()
StackExchange.snippets.init();
);
);
, "code-snippets");

StackExchange.ready(function()
var channelOptions =
tags: "".split(" "),
id: "1"
;
initTagRenderer("".split(" "), "".split(" "), channelOptions);

StackExchange.using("externalEditor", function()
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled)
StackExchange.using("snippets", function()
createEditor();
);

else
createEditor();

);

function createEditor()
StackExchange.prepareEditor(
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader:
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
,
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
);



);

draft saved

draft discarded

Sign up or log in

StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);

Sign up using Google

Sign up using Facebook

Sign up using Email and Password

Post as a guest

Name

Email

Required, but never shown

StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f57254964%2fincrementing-add-under-condition-in-pandas%23new-answer', 'question_page');

);

Post as a guest

Name

Email

Required, but never shown

7

Use cumsum and mask:

df['E_output'] = df['servo_in_position'].diff().eq(1).cumsum()
 .mask(df['servo_in_position'] == 0, 0)

Output:

 servo_in_position second_servo_in_position Expected output E_output
0 0 1 0 0
1 0 1 0 0
2 1 2 1 1
3 0 3 0 0
4 1 4 2 2
5 1 4 2 2
6 0 5 0 0
7 0 5 0 0
8 1 6 3 3
9 0 7 0 0
10 1 8 4 4
11 0 9 0 0
12 1 10 5 5
13 1 10 5 5
14 1 10 5 5
15 0 11 0 0
16 0 11 0 0
17 0 11 0 0
18 1 12 6 6
19 1 12 6 6
20 0 13 0 0
21 0 13 0 0
22 0 13 0 0

share|improve this answer

answered 9 hours ago

Scott BostonScott Boston

64.7k77 gold badges3838 silver badges6262 bronze badges

add a comment | 

7

Use cumsum and mask:

df['E_output'] = df['servo_in_position'].diff().eq(1).cumsum()
 .mask(df['servo_in_position'] == 0, 0)

Output:

 servo_in_position second_servo_in_position Expected output E_output
0 0 1 0 0
1 0 1 0 0
2 1 2 1 1
3 0 3 0 0
4 1 4 2 2
5 1 4 2 2
6 0 5 0 0
7 0 5 0 0
8 1 6 3 3
9 0 7 0 0
10 1 8 4 4
11 0 9 0 0
12 1 10 5 5
13 1 10 5 5
14 1 10 5 5
15 0 11 0 0
16 0 11 0 0
17 0 11 0 0
18 1 12 6 6
19 1 12 6 6
20 0 13 0 0
21 0 13 0 0
22 0 13 0 0

share|improve this answer

answered 9 hours ago

Scott BostonScott Boston

64.7k77 gold badges3838 silver badges6262 bronze badges

add a comment | 

Use cumsum and mask:

df['E_output'] = df['servo_in_position'].diff().eq(1).cumsum()
 .mask(df['servo_in_position'] == 0, 0)

Output:

 servo_in_position second_servo_in_position Expected output E_output
0 0 1 0 0
1 0 1 0 0
2 1 2 1 1
3 0 3 0 0
4 1 4 2 2
5 1 4 2 2
6 0 5 0 0
7 0 5 0 0
8 1 6 3 3
9 0 7 0 0
10 1 8 4 4
11 0 9 0 0
12 1 10 5 5
13 1 10 5 5
14 1 10 5 5
15 0 11 0 0
16 0 11 0 0
17 0 11 0 0
18 1 12 6 6
19 1 12 6 6
20 0 13 0 0
21 0 13 0 0
22 0 13 0 0

share|improve this answer

answered 9 hours ago

Scott BostonScott Boston

64.7k77 gold badges3838 silver badges6262 bronze badges

df['E_output'] = df['servo_in_position'].diff().eq(1).cumsum()
 .mask(df['servo_in_position'] == 0, 0)

 servo_in_position second_servo_in_position Expected output E_output
0 0 1 0 0
1 0 1 0 0
2 1 2 1 1
3 0 3 0 0
4 1 4 2 2
5 1 4 2 2
6 0 5 0 0
7 0 5 0 0
8 1 6 3 3
9 0 7 0 0
10 1 8 4 4
11 0 9 0 0
12 1 10 5 5
13 1 10 5 5
14 1 10 5 5
15 0 11 0 0
16 0 11 0 0
17 0 11 0 0
18 1 12 6 6
19 1 12 6 6
20 0 13 0 0
21 0 13 0 0
22 0 13 0 0

share|improve this answer

answered 9 hours ago

Scott BostonScott Boston

64.7k77 gold badges3838 silver badges6262 bronze badges

answered 9 hours ago

Scott BostonScott Boston

64.7k77 gold badges3838 silver badges6262 bronze badges

answered 9 hours ago

Scott BostonScott Boston

64.7k77 gold badges3838 silver badges6262 bronze badges

6

Try np.where:

df['Expected_output'] = np.where(df.servo_in_position.eq(1),
 df.servo_in_position.diff().eq(1).cumsum(),
 0)

share|improve this answer

answered 9 hours ago

Quang HoangQuang Hoang

14.4k22 gold badges1414 silver badges2424 bronze badges

add a comment | 

6

Try np.where:

df['Expected_output'] = np.where(df.servo_in_position.eq(1),
 df.servo_in_position.diff().eq(1).cumsum(),
 0)

share|improve this answer

answered 9 hours ago

Quang HoangQuang Hoang

14.4k22 gold badges1414 silver badges2424 bronze badges

add a comment | 

Try np.where:

df['Expected_output'] = np.where(df.servo_in_position.eq(1),
 df.servo_in_position.diff().eq(1).cumsum(),
 0)

share|improve this answer

answered 9 hours ago

Quang HoangQuang Hoang

14.4k22 gold badges1414 silver badges2424 bronze badges

df['Expected_output'] = np.where(df.servo_in_position.eq(1),
 df.servo_in_position.diff().eq(1).cumsum(),
 0)

share|improve this answer

answered 9 hours ago

Quang HoangQuang Hoang

14.4k22 gold badges1414 silver badges2424 bronze badges

answered 9 hours ago

Quang HoangQuang Hoang

14.4k22 gold badges1414 silver badges2424 bronze badges

answered 9 hours ago

Quang HoangQuang Hoang

14.4k22 gold badges1414 silver badges2424 bronze badges

6

Using cumsum and arithmetic.

---

u = df['servo_in_position']

(u.eq(1) & u.shift().ne(1)).cumsum() * u

0 0
1 0
2 1
3 0
4 2
5 2
6 0
7 0
8 3
9 0
10 4
11 0
12 5
13 5
14 5
15 0
16 0
17 0
18 6
19 6
20 0
21 0
22 0
Name: servo_in_position, dtype: int64

share|improve this answer

answered 9 hours ago

user3483203user3483203

36.7k88 gold badges3131 silver badges6060 bronze badges

add a comment | 

6

Using cumsum and arithmetic.

---

u = df['servo_in_position']

(u.eq(1) & u.shift().ne(1)).cumsum() * u

0 0
1 0
2 1
3 0
4 2
5 2
6 0
7 0
8 3
9 0
10 4
11 0
12 5
13 5
14 5
15 0
16 0
17 0
18 6
19 6
20 0
21 0
22 0
Name: servo_in_position, dtype: int64

share|improve this answer

answered 9 hours ago

user3483203user3483203

36.7k88 gold badges3131 silver badges6060 bronze badges

add a comment | 

Using cumsum and arithmetic.

---

u = df['servo_in_position']

(u.eq(1) & u.shift().ne(1)).cumsum() * u

0 0
1 0
2 1
3 0
4 2
5 2
6 0
7 0
8 3
9 0
10 4
11 0
12 5
13 5
14 5
15 0
16 0
17 0
18 6
19 6
20 0
21 0
22 0
Name: servo_in_position, dtype: int64

share|improve this answer

answered 9 hours ago

user3483203user3483203

36.7k88 gold badges3131 silver badges6060 bronze badges

u = df['servo_in_position']

(u.eq(1) & u.shift().ne(1)).cumsum() * u

0 0
1 0
2 1
3 0
4 2
5 2
6 0
7 0
8 3
9 0
10 4
11 0
12 5
13 5
14 5
15 0
16 0
17 0
18 6
19 6
20 0
21 0
22 0
Name: servo_in_position, dtype: int64

share|improve this answer

answered 9 hours ago

user3483203user3483203

36.7k88 gold badges3131 silver badges6060 bronze badges

answered 9 hours ago

user3483203user3483203

36.7k88 gold badges3131 silver badges6060 bronze badges

answered 9 hours ago

user3483203user3483203

36.7k88 gold badges3131 silver badges6060 bronze badges

5

That is cumsum and mul

df.servo_in_position.diff().eq(1).cumsum().mul(df.servo_in_position.eq(1),axis=0)

share|improve this answer

answered 9 hours ago

WeNYoBenWeNYoBen

150k88 gold badges5353 silver badges8484 bronze badges

add a comment | 

5

That is cumsum and mul

df.servo_in_position.diff().eq(1).cumsum().mul(df.servo_in_position.eq(1),axis=0)

share|improve this answer

answered 9 hours ago

WeNYoBenWeNYoBen

150k88 gold badges5353 silver badges8484 bronze badges

add a comment | 

That is cumsum and mul

df.servo_in_position.diff().eq(1).cumsum().mul(df.servo_in_position.eq(1),axis=0)

share|improve this answer

answered 9 hours ago

WeNYoBenWeNYoBen

150k88 gold badges5353 silver badges8484 bronze badges

df.servo_in_position.diff().eq(1).cumsum().mul(df.servo_in_position.eq(1),axis=0)

share|improve this answer

answered 9 hours ago

WeNYoBenWeNYoBen

150k88 gold badges5353 silver badges8484 bronze badges

answered 9 hours ago

WeNYoBenWeNYoBen

150k88 gold badges5353 silver badges8484 bronze badges

answered 9 hours ago

WeNYoBenWeNYoBen

150k88 gold badges5353 silver badges8484 bronze badges

3

Fast with Numba

from numba import njit

@njit
def f(u):
 out = np.zeros(len(u), np.int64)
 a = out[0] = u[0]
 for i in range(1, len(u)):
 if u[i] == 1:
 if u[i - 1] == 0:
 a += 1
 out[i] = a
 return out

f(df.servo_in_position.to_numpy())

array([0, 0, 1, 0, 2, 2, 0, 0, 3, 0, 4, 0, 5, 5, 5, 0, 0, 0, 6, 6, 0, 0, 0])

share|improve this answer

answered 7 hours ago

piRSquaredpiRSquared

174k2626 gold badges188188 silver badges343343 bronze badges

add a comment | 

3

Fast with Numba

from numba import njit

@njit
def f(u):
 out = np.zeros(len(u), np.int64)
 a = out[0] = u[0]
 for i in range(1, len(u)):
 if u[i] == 1:
 if u[i - 1] == 0:
 a += 1
 out[i] = a
 return out

f(df.servo_in_position.to_numpy())

array([0, 0, 1, 0, 2, 2, 0, 0, 3, 0, 4, 0, 5, 5, 5, 0, 0, 0, 6, 6, 0, 0, 0])

share|improve this answer

answered 7 hours ago

piRSquaredpiRSquared

174k2626 gold badges188188 silver badges343343 bronze badges

add a comment | 

Fast with Numba

from numba import njit

@njit
def f(u):
 out = np.zeros(len(u), np.int64)
 a = out[0] = u[0]
 for i in range(1, len(u)):
 if u[i] == 1:
 if u[i - 1] == 0:
 a += 1
 out[i] = a
 return out

f(df.servo_in_position.to_numpy())

array([0, 0, 1, 0, 2, 2, 0, 0, 3, 0, 4, 0, 5, 5, 5, 0, 0, 0, 6, 6, 0, 0, 0])

share|improve this answer

answered 7 hours ago

piRSquaredpiRSquared

174k2626 gold badges188188 silver badges343343 bronze badges

from numba import njit

@njit
def f(u):
 out = np.zeros(len(u), np.int64)
 a = out[0] = u[0]
 for i in range(1, len(u)):
 if u[i] == 1:
 if u[i - 1] == 0:
 a += 1
 out[i] = a
 return out

f(df.servo_in_position.to_numpy())

array([0, 0, 1, 0, 2, 2, 0, 0, 3, 0, 4, 0, 5, 5, 5, 0, 0, 0, 6, 6, 0, 0, 0])

share|improve this answer

answered 7 hours ago

piRSquaredpiRSquared

174k2626 gold badges188188 silver badges343343 bronze badges

answered 7 hours ago

piRSquaredpiRSquared

174k2626 gold badges188188 silver badges343343 bronze badges

answered 7 hours ago

piRSquaredpiRSquared

174k2626 gold badges188188 silver badges343343 bronze badges