How to ignore dtype when read file csv using Dask Dataframe
1
I have a large file csv, it has 9600 columns and each column has a different type. When I read file using Dask Datafame and use attribute head(), I get error Mismatched dtypes found in pd.read_csv/pd.read_table. How can I ignore it. I use pandas read file csv don't have errors but very slow because the size of file is 2.5GB.
Thank!
python pandas dask
edited Nov 21 '18 at 15:09
leoburgy
1107
asked Nov 21 '18 at 14:47
Hoàng Quốc CườngHoàng Quốc Cường
62
|
show 3 more comments
1
I have a large file csv, it has 9600 columns and each column has a different type. When I read file using Dask Datafame and use attribute head(), I get error Mismatched dtypes found in pd.read_csv/pd.read_table. How can I ignore it. I use pandas read file csv don't have errors but very slow because the size of file is 2.5GB.
Thank!
python pandas dask
edited Nov 21 '18 at 15:09
leoburgy
1107
asked Nov 21 '18 at 14:47
Hoàng Quốc CườngHoàng Quốc Cường
62
1
That's a lot of columns! How many rows?
– mdurant
Nov 21 '18 at 14:50
Could you provide a sample of your csv file? along with code to import the file?
– leoburgy
Nov 21 '18 at 14:58
1
depending on your degree of knowledge about the potential data type present in the data set, you may want to cast the data type to string by setting thedtypeargument in theread_csv()function.
– leoburgy
Nov 21 '18 at 15:03
In the foot note ofDaskdoc (), you can read that despite the inference about the data type, the presence of NaN can confuse the csv reader function. docs.dask.org/en/latest/…
– leoburgy
Nov 21 '18 at 15:06
1
^ this doesn't tell us much. You said that the types were string or null, so explicitly loading as str sounds ok, but you have more information than we do.
– mdurant
Nov 21 '18 at 15:47
|
show 3 more comments
1
1
1
I have a large file csv, it has 9600 columns and each column has a different type. When I read file using Dask Datafame and use attribute head(), I get error Mismatched dtypes found in pd.read_csv/pd.read_table. How can I ignore it. I use pandas read file csv don't have errors but very slow because the size of file is 2.5GB.
Thank!
python pandas dask
edited Nov 21 '18 at 15:09
leoburgy
1107
asked Nov 21 '18 at 14:47
Hoàng Quốc CườngHoàng Quốc Cường
62
I have a large file csv, it has 9600 columns and each column has a different type. When I read file using Dask Datafame and use attribute head(), I get error Mismatched dtypes found in pd.read_csv/pd.read_table. How can I ignore it. I use pandas read file csv don't have errors but very slow because the size of file is 2.5GB.
Thank!
python pandas dask
python pandas dask
edited Nov 21 '18 at 15:09
leoburgy
1107
asked Nov 21 '18 at 14:47
Hoàng Quốc CườngHoàng Quốc Cường
62
edited Nov 21 '18 at 15:09
leoburgy
1107
asked Nov 21 '18 at 14:47
Hoàng Quốc CườngHoàng Quốc Cường
62
edited Nov 21 '18 at 15:09
leoburgy
1107
edited Nov 21 '18 at 15:09
leoburgy
1107
edited Nov 21 '18 at 15:09
leoburgy
1107
1107
asked Nov 21 '18 at 14:47
Hoàng Quốc CườngHoàng Quốc Cường
62
asked Nov 21 '18 at 14:47
Hoàng Quốc CườngHoàng Quốc Cường
62
asked Nov 21 '18 at 14:47
Hoàng Quốc CườngHoàng Quốc Cường
62
62
1
That's a lot of columns! How many rows?
– mdurant
Nov 21 '18 at 14:50
Could you provide a sample of your csv file? along with code to import the file?
– leoburgy
Nov 21 '18 at 14:58
1
depending on your degree of knowledge about the potential data type present in the data set, you may want to cast the data type to string by setting thedtypeargument in theread_csv()function.
– leoburgy
Nov 21 '18 at 15:03
In the foot note ofDaskdoc (), you can read that despite the inference about the data type, the presence of NaN can confuse the csv reader function. docs.dask.org/en/latest/…
– leoburgy
Nov 21 '18 at 15:06
1
^ this doesn't tell us much. You said that the types were string or null, so explicitly loading as str sounds ok, but you have more information than we do.
– mdurant
Nov 21 '18 at 15:47
|
show 3 more comments
1
That's a lot of columns! How many rows?
– mdurant
Nov 21 '18 at 14:50
Could you provide a sample of your csv file? along with code to import the file?
– leoburgy
Nov 21 '18 at 14:58
1
depending on your degree of knowledge about the potential data type present in the data set, you may want to cast the data type to string by setting thedtypeargument in theread_csv()function.
– leoburgy
Nov 21 '18 at 15:03
In the foot note ofDaskdoc (), you can read that despite the inference about the data type, the presence of NaN can confuse the csv reader function. docs.dask.org/en/latest/…
– leoburgy
Nov 21 '18 at 15:06
1
^ this doesn't tell us much. You said that the types were string or null, so explicitly loading as str sounds ok, but you have more information than we do.
– mdurant
Nov 21 '18 at 15:47
1
1
That's a lot of columns! How many rows?
– mdurant
Nov 21 '18 at 14:50
That's a lot of columns! How many rows?
– mdurant
Nov 21 '18 at 14:50
Could you provide a sample of your csv file? along with code to import the file?
– leoburgy
Nov 21 '18 at 14:58
Could you provide a sample of your csv file? along with code to import the file?
– leoburgy
Nov 21 '18 at 14:58
1
1
depending on your degree of knowledge about the potential data type present in the data set, you may want to cast the data type to string by setting the
dtype argument in the read_csv() function.– leoburgy
Nov 21 '18 at 15:03
depending on your degree of knowledge about the potential data type present in the data set, you may want to cast the data type to string by setting the
dtype argument in the read_csv() function.– leoburgy
Nov 21 '18 at 15:03
In the foot note of
Dask doc (), you can read that despite the inference about the data type, the presence of NaN can confuse the csv reader function. docs.dask.org/en/latest/…– leoburgy
Nov 21 '18 at 15:06
In the foot note of
Dask doc (), you can read that despite the inference about the data type, the presence of NaN can confuse the csv reader function. docs.dask.org/en/latest/…– leoburgy
Nov 21 '18 at 15:06
1
1
^ this doesn't tell us much. You said that the types were string or null, so explicitly loading as str sounds ok, but you have more information than we do.
– mdurant
Nov 21 '18 at 15:47
^ this doesn't tell us much. You said that the types were string or null, so explicitly loading as str sounds ok, but you have more information than we do.
– mdurant
Nov 21 '18 at 15:47
|
show 3 more comments
0
active
oldest
votes
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
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53414613%2fhow-to-ignore-dtype-when-read-file-csv-using-dask-dataframe%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
0
active
oldest
votes
0
active
oldest
votes
active
oldest
votes
active
oldest
votes
draft saved
draft discarded
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.
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
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53414613%2fhow-to-ignore-dtype-when-read-file-csv-using-dask-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
1
That's a lot of columns! How many rows?
– mdurant
Nov 21 '18 at 14:50
Could you provide a sample of your csv file? along with code to import the file?
– leoburgy
Nov 21 '18 at 14:58
1
depending on your degree of knowledge about the potential data type present in the data set, you may want to cast the data type to string by setting the
dtypeargument in theread_csv()function.– leoburgy
Nov 21 '18 at 15:03
In the foot note of
Daskdoc (), you can read that despite the inference about the data type, the presence of NaN can confuse the csv reader function. docs.dask.org/en/latest/…– leoburgy
Nov 21 '18 at 15:06
1
^ this doesn't tell us much. You said that the types were string or null, so explicitly loading as str sounds ok, but you have more information than we do.
– mdurant
Nov 21 '18 at 15:47