numbering elements in a pandas dataframe
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty{ height:90px;width:728px;box-sizing:border-box;
}
I am trying to build a small stock trading reporting in pandas. It's getting a little complicated because of subsequent buys and sells.
Assuming I have my buys and sells in a dataframe:
import pandas as pd
data = pd.read_csv("ticker1.csv", delimiter=";")
data['cumsum']=data['quantity'].cumsum(axis=0)
data
Date qty price cumsum
0 2018-01-20 80 20.70 80
1 2018-02-14 90 20.82 170
2 2018-02-19 -100 20.62 70
3 2018-02-27 -70 20.55 0
4 2018-03-13 30 19.80 30
5 2018-03-14 10 19.55 40
6 2018-03-30 -20 20.92 20
7 2018-04-01 -10 20.95 10
8 2018-04-10 -10 21.03 0
9 2018-05-04 25 19.77 25
10 2018-05-31 -10 20.22 15
So there can be "completed" cycles of buying and selling whenever cumsum =0 (no short-selling). In this example, there would be an open position of 15 at the end.
In order to analyze the trades, I'd like to group them like this:
Date qty price cumsum group
0 2018-01-20 80 20.70 80 1
1 2018-02-14 90 20.82 170 1
2 2018-02-19 -100 20.62 70 1
3 2018-02-27 -70 20.55 0 1
4 2018-03-13 30 19.80 30 2
5 2018-03-14 10 19.55 40 2
6 2018-03-30 -20 20.92 20 2
7 2018-04-01 -10 20.95 10 2
8 2018-04-10 -10 21.03 0 2
9 2018-05-04 25 19.77 25 3
10 2018-05-31 -10 20.22 15 3
I am trying to group the transactions until the next time cumsum =0.
Then I could loop over the groupings for further analysis (e.g. see if it was a winning or losing trade, # days between first buy and last sale etc.) and I would be able to see that in this case there is an open position at the moment (if last value for cumsum != 0).
Could someone please give me a hint how I could realize the grouping?
Thanks
python pandas dataframe
asked Nov 24 '18 at 17:13
RazzleDazzleRazzleDazzle
237
add a comment |
I am trying to build a small stock trading reporting in pandas. It's getting a little complicated because of subsequent buys and sells.
Assuming I have my buys and sells in a dataframe:
import pandas as pd
data = pd.read_csv("ticker1.csv", delimiter=";")
data['cumsum']=data['quantity'].cumsum(axis=0)
data
Date qty price cumsum
0 2018-01-20 80 20.70 80
1 2018-02-14 90 20.82 170
2 2018-02-19 -100 20.62 70
3 2018-02-27 -70 20.55 0
4 2018-03-13 30 19.80 30
5 2018-03-14 10 19.55 40
6 2018-03-30 -20 20.92 20
7 2018-04-01 -10 20.95 10
8 2018-04-10 -10 21.03 0
9 2018-05-04 25 19.77 25
10 2018-05-31 -10 20.22 15
So there can be "completed" cycles of buying and selling whenever cumsum =0 (no short-selling). In this example, there would be an open position of 15 at the end.
In order to analyze the trades, I'd like to group them like this:
Date qty price cumsum group
0 2018-01-20 80 20.70 80 1
1 2018-02-14 90 20.82 170 1
2 2018-02-19 -100 20.62 70 1
3 2018-02-27 -70 20.55 0 1
4 2018-03-13 30 19.80 30 2
5 2018-03-14 10 19.55 40 2
6 2018-03-30 -20 20.92 20 2
7 2018-04-01 -10 20.95 10 2
8 2018-04-10 -10 21.03 0 2
9 2018-05-04 25 19.77 25 3
10 2018-05-31 -10 20.22 15 3
I am trying to group the transactions until the next time cumsum =0.
Then I could loop over the groupings for further analysis (e.g. see if it was a winning or losing trade, # days between first buy and last sale etc.) and I would be able to see that in this case there is an open position at the moment (if last value for cumsum != 0).
Could someone please give me a hint how I could realize the grouping?
Thanks
python pandas dataframe
asked Nov 24 '18 at 17:13
RazzleDazzleRazzleDazzle
237
add a comment |
I am trying to build a small stock trading reporting in pandas. It's getting a little complicated because of subsequent buys and sells.
Assuming I have my buys and sells in a dataframe:
import pandas as pd
data = pd.read_csv("ticker1.csv", delimiter=";")
data['cumsum']=data['quantity'].cumsum(axis=0)
data
Date qty price cumsum
0 2018-01-20 80 20.70 80
1 2018-02-14 90 20.82 170
2 2018-02-19 -100 20.62 70
3 2018-02-27 -70 20.55 0
4 2018-03-13 30 19.80 30
5 2018-03-14 10 19.55 40
6 2018-03-30 -20 20.92 20
7 2018-04-01 -10 20.95 10
8 2018-04-10 -10 21.03 0
9 2018-05-04 25 19.77 25
10 2018-05-31 -10 20.22 15
So there can be "completed" cycles of buying and selling whenever cumsum =0 (no short-selling). In this example, there would be an open position of 15 at the end.
In order to analyze the trades, I'd like to group them like this:
Date qty price cumsum group
0 2018-01-20 80 20.70 80 1
1 2018-02-14 90 20.82 170 1
2 2018-02-19 -100 20.62 70 1
3 2018-02-27 -70 20.55 0 1
4 2018-03-13 30 19.80 30 2
5 2018-03-14 10 19.55 40 2
6 2018-03-30 -20 20.92 20 2
7 2018-04-01 -10 20.95 10 2
8 2018-04-10 -10 21.03 0 2
9 2018-05-04 25 19.77 25 3
10 2018-05-31 -10 20.22 15 3
I am trying to group the transactions until the next time cumsum =0.
Then I could loop over the groupings for further analysis (e.g. see if it was a winning or losing trade, # days between first buy and last sale etc.) and I would be able to see that in this case there is an open position at the moment (if last value for cumsum != 0).
Could someone please give me a hint how I could realize the grouping?
Thanks
python pandas dataframe
asked Nov 24 '18 at 17:13
RazzleDazzleRazzleDazzle
237
I am trying to build a small stock trading reporting in pandas. It's getting a little complicated because of subsequent buys and sells.
Assuming I have my buys and sells in a dataframe:
import pandas as pd
data = pd.read_csv("ticker1.csv", delimiter=";")
data['cumsum']=data['quantity'].cumsum(axis=0)
data
Date qty price cumsum
0 2018-01-20 80 20.70 80
1 2018-02-14 90 20.82 170
2 2018-02-19 -100 20.62 70
3 2018-02-27 -70 20.55 0
4 2018-03-13 30 19.80 30
5 2018-03-14 10 19.55 40
6 2018-03-30 -20 20.92 20
7 2018-04-01 -10 20.95 10
8 2018-04-10 -10 21.03 0
9 2018-05-04 25 19.77 25
10 2018-05-31 -10 20.22 15
So there can be "completed" cycles of buying and selling whenever cumsum =0 (no short-selling). In this example, there would be an open position of 15 at the end.
In order to analyze the trades, I'd like to group them like this:
Date qty price cumsum group
0 2018-01-20 80 20.70 80 1
1 2018-02-14 90 20.82 170 1
2 2018-02-19 -100 20.62 70 1
3 2018-02-27 -70 20.55 0 1
4 2018-03-13 30 19.80 30 2
5 2018-03-14 10 19.55 40 2
6 2018-03-30 -20 20.92 20 2
7 2018-04-01 -10 20.95 10 2
8 2018-04-10 -10 21.03 0 2
9 2018-05-04 25 19.77 25 3
10 2018-05-31 -10 20.22 15 3
I am trying to group the transactions until the next time cumsum =0.
Then I could loop over the groupings for further analysis (e.g. see if it was a winning or losing trade, # days between first buy and last sale etc.) and I would be able to see that in this case there is an open position at the moment (if last value for cumsum != 0).
Could someone please give me a hint how I could realize the grouping?
Thanks
python pandas dataframe
python pandas dataframe
asked Nov 24 '18 at 17:13
RazzleDazzleRazzleDazzle
237
asked Nov 24 '18 at 17:13
RazzleDazzleRazzleDazzle
237
asked Nov 24 '18 at 17:13
RazzleDazzleRazzleDazzle
237
asked Nov 24 '18 at 17:13
RazzleDazzleRazzleDazzle
237
asked Nov 24 '18 at 17:13
RazzleDazzleRazzleDazzle
237
237
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Coincidentally, one solution is to apply Series.cumsum() on the column named cumsum:
df['group'] = (df['cumsum'].shift() == 0).astype(int).cumsum() + 1
df
Date qty price cumsum group
0 2018-01-20 80 20.70 80 1
1 2018-02-14 90 20.82 170 1
2 2018-02-19 -100 20.62 70 1
3 2018-02-27 -70 20.55 0 1
4 2018-03-13 30 19.80 30 2
5 2018-03-14 10 19.55 40 2
6 2018-03-30 -20 20.92 20 2
7 2018-04-01 -10 20.95 10 2
8 2018-04-10 -10 21.03 0 2
9 2018-05-04 25 19.77 25 3
10 2018-05-31 -10 20.22 15 3
answered Nov 24 '18 at 17:17
Peter LeimbiglerPeter Leimbigler
4,8371416
Thanks - that’s really an elegant way to this! I would not have thought of that myself!
– RazzleDazzle
Nov 24 '18 at 18:05
@RazzleDazzle, you're welcome! You'll learn fast as you continue writing and reading code, and this will become second nature to you :)
– Peter Leimbigler
Nov 24 '18 at 18:31
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
});
}
});
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
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53460558%2fnumbering-elements-in-a-pandas-dataframe%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Coincidentally, one solution is to apply Series.cumsum() on the column named cumsum:
df['group'] = (df['cumsum'].shift() == 0).astype(int).cumsum() + 1
df
Date qty price cumsum group
0 2018-01-20 80 20.70 80 1
1 2018-02-14 90 20.82 170 1
2 2018-02-19 -100 20.62 70 1
3 2018-02-27 -70 20.55 0 1
4 2018-03-13 30 19.80 30 2
5 2018-03-14 10 19.55 40 2
6 2018-03-30 -20 20.92 20 2
7 2018-04-01 -10 20.95 10 2
8 2018-04-10 -10 21.03 0 2
9 2018-05-04 25 19.77 25 3
10 2018-05-31 -10 20.22 15 3
answered Nov 24 '18 at 17:17
Peter LeimbiglerPeter Leimbigler
4,8371416
Thanks - that’s really an elegant way to this! I would not have thought of that myself!
– RazzleDazzle
Nov 24 '18 at 18:05
@RazzleDazzle, you're welcome! You'll learn fast as you continue writing and reading code, and this will become second nature to you :)
– Peter Leimbigler
Nov 24 '18 at 18:31
add a comment |
Coincidentally, one solution is to apply Series.cumsum() on the column named cumsum:
df['group'] = (df['cumsum'].shift() == 0).astype(int).cumsum() + 1
df
Date qty price cumsum group
0 2018-01-20 80 20.70 80 1
1 2018-02-14 90 20.82 170 1
2 2018-02-19 -100 20.62 70 1
3 2018-02-27 -70 20.55 0 1
4 2018-03-13 30 19.80 30 2
5 2018-03-14 10 19.55 40 2
6 2018-03-30 -20 20.92 20 2
7 2018-04-01 -10 20.95 10 2
8 2018-04-10 -10 21.03 0 2
9 2018-05-04 25 19.77 25 3
10 2018-05-31 -10 20.22 15 3
answered Nov 24 '18 at 17:17
Peter LeimbiglerPeter Leimbigler
4,8371416
Thanks - that’s really an elegant way to this! I would not have thought of that myself!
– RazzleDazzle
Nov 24 '18 at 18:05
@RazzleDazzle, you're welcome! You'll learn fast as you continue writing and reading code, and this will become second nature to you :)
– Peter Leimbigler
Nov 24 '18 at 18:31
add a comment |
Coincidentally, one solution is to apply Series.cumsum() on the column named cumsum:
df['group'] = (df['cumsum'].shift() == 0).astype(int).cumsum() + 1
df
Date qty price cumsum group
0 2018-01-20 80 20.70 80 1
1 2018-02-14 90 20.82 170 1
2 2018-02-19 -100 20.62 70 1
3 2018-02-27 -70 20.55 0 1
4 2018-03-13 30 19.80 30 2
5 2018-03-14 10 19.55 40 2
6 2018-03-30 -20 20.92 20 2
7 2018-04-01 -10 20.95 10 2
8 2018-04-10 -10 21.03 0 2
9 2018-05-04 25 19.77 25 3
10 2018-05-31 -10 20.22 15 3
answered Nov 24 '18 at 17:17
Peter LeimbiglerPeter Leimbigler
4,8371416
Coincidentally, one solution is to apply Series.cumsum() on the column named cumsum:
df['group'] = (df['cumsum'].shift() == 0).astype(int).cumsum() + 1
df
Date qty price cumsum group
0 2018-01-20 80 20.70 80 1
1 2018-02-14 90 20.82 170 1
2 2018-02-19 -100 20.62 70 1
3 2018-02-27 -70 20.55 0 1
4 2018-03-13 30 19.80 30 2
5 2018-03-14 10 19.55 40 2
6 2018-03-30 -20 20.92 20 2
7 2018-04-01 -10 20.95 10 2
8 2018-04-10 -10 21.03 0 2
9 2018-05-04 25 19.77 25 3
10 2018-05-31 -10 20.22 15 3
answered Nov 24 '18 at 17:17
Peter LeimbiglerPeter Leimbigler
4,8371416
answered Nov 24 '18 at 17:17
Peter LeimbiglerPeter Leimbigler
4,8371416
answered Nov 24 '18 at 17:17
Peter LeimbiglerPeter Leimbigler
4,8371416
answered Nov 24 '18 at 17:17
Peter LeimbiglerPeter Leimbigler
4,8371416
4,8371416
Thanks - that’s really an elegant way to this! I would not have thought of that myself!
– RazzleDazzle
Nov 24 '18 at 18:05
@RazzleDazzle, you're welcome! You'll learn fast as you continue writing and reading code, and this will become second nature to you :)
– Peter Leimbigler
Nov 24 '18 at 18:31
add a comment |
Thanks - that’s really an elegant way to this! I would not have thought of that myself!
– RazzleDazzle
Nov 24 '18 at 18:05
@RazzleDazzle, you're welcome! You'll learn fast as you continue writing and reading code, and this will become second nature to you :)
– Peter Leimbigler
Nov 24 '18 at 18:31
Thanks - that’s really an elegant way to this! I would not have thought of that myself!
– RazzleDazzle
Nov 24 '18 at 18:05
Thanks - that’s really an elegant way to this! I would not have thought of that myself!
– RazzleDazzle
Nov 24 '18 at 18:05
@RazzleDazzle, you're welcome! You'll learn fast as you continue writing and reading code, and this will become second nature to you :)
– Peter Leimbigler
Nov 24 '18 at 18:31
@RazzleDazzle, you're welcome! You'll learn fast as you continue writing and reading code, and this will become second nature to you :)
– Peter Leimbigler
Nov 24 '18 at 18:31
add a comment |
Thanks for contributing an answer to Stack Overflow!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
To learn more, see our tips on writing great answers.
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
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53460558%2fnumbering-elements-in-a-pandas-dataframe%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
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
Required, but never shown
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
Required, but never shown
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
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
