How to properly use groupby on pandas
up vote
0
down vote
favorite
I started to work in a startup, but I'm the only data analyst, so I can't ask in my company, my problem is that in a lot of parts of my code I have to divide the dataframe, so I use the groupby to:
for name, group in mvtos_material_df.groupby('Referencia'):
# Work with the groups
But I think that working with for loops on pandas is wrong, and for some easy functions I saw that the idea is to use groupby().apply() but I have also the next code which I don't how to fix it, if not putting it all in a method, but seems a wrong idea.
moves_dict = {}
for name, group in mvtos_material_df.groupby('Reference'):
moves_dict[str(name)] = pd.DataFrame()
# for loop to fill moves_dict with the orders per day
for k, v in group.groupby(pd.Grouper(key='Date', freq='M')):
if not v.empty:
# comparing dates in inventory and movements to get the final stock
a = (inventory_df[inventory_df['REFERENCE'].values == v.tail(1)['Reference'].values])
a = (a[a['Date'].dt.month.values == v.tail(1)['Date'].dt.month.values])
a = (a[a['Date'].dt.year.values == v.tail(1)['Date'].dt.year.values])
# sum all days together and create the ones which are missing
for i, j in v.groupby(pd.Grouper(key='Date', freq='D')):
temp = j.tail(1).copy() # We need to get the last row
moves_dict[str(name)] = moves_dict[str(name)].append(j.tail(1))
if not a.empty:
# Drop last row (last day of the month) and add the one with the final stock
moves_dict[str(name)].drop(moves_dict[str(name)].index[len(moves_dict[str(name)])-1], inplace = True)
temp['final_stock'] = float(a['FINAL STOCK'])
moves_dict[str(name)] = moves_dict[str(name)].append(temp)
python performance pandas
New contributor
add a comment |
up vote
0
down vote
favorite
I started to work in a startup, but I'm the only data analyst, so I can't ask in my company, my problem is that in a lot of parts of my code I have to divide the dataframe, so I use the groupby to:
for name, group in mvtos_material_df.groupby('Referencia'):
# Work with the groups
But I think that working with for loops on pandas is wrong, and for some easy functions I saw that the idea is to use groupby().apply() but I have also the next code which I don't how to fix it, if not putting it all in a method, but seems a wrong idea.
moves_dict = {}
for name, group in mvtos_material_df.groupby('Reference'):
moves_dict[str(name)] = pd.DataFrame()
# for loop to fill moves_dict with the orders per day
for k, v in group.groupby(pd.Grouper(key='Date', freq='M')):
if not v.empty:
# comparing dates in inventory and movements to get the final stock
a = (inventory_df[inventory_df['REFERENCE'].values == v.tail(1)['Reference'].values])
a = (a[a['Date'].dt.month.values == v.tail(1)['Date'].dt.month.values])
a = (a[a['Date'].dt.year.values == v.tail(1)['Date'].dt.year.values])
# sum all days together and create the ones which are missing
for i, j in v.groupby(pd.Grouper(key='Date', freq='D')):
temp = j.tail(1).copy() # We need to get the last row
moves_dict[str(name)] = moves_dict[str(name)].append(j.tail(1))
if not a.empty:
# Drop last row (last day of the month) and add the one with the final stock
moves_dict[str(name)].drop(moves_dict[str(name)].index[len(moves_dict[str(name)])-1], inplace = True)
temp['final_stock'] = float(a['FINAL STOCK'])
moves_dict[str(name)] = moves_dict[str(name)].append(temp)
python performance pandas
New contributor
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I started to work in a startup, but I'm the only data analyst, so I can't ask in my company, my problem is that in a lot of parts of my code I have to divide the dataframe, so I use the groupby to:
for name, group in mvtos_material_df.groupby('Referencia'):
# Work with the groups
But I think that working with for loops on pandas is wrong, and for some easy functions I saw that the idea is to use groupby().apply() but I have also the next code which I don't how to fix it, if not putting it all in a method, but seems a wrong idea.
moves_dict = {}
for name, group in mvtos_material_df.groupby('Reference'):
moves_dict[str(name)] = pd.DataFrame()
# for loop to fill moves_dict with the orders per day
for k, v in group.groupby(pd.Grouper(key='Date', freq='M')):
if not v.empty:
# comparing dates in inventory and movements to get the final stock
a = (inventory_df[inventory_df['REFERENCE'].values == v.tail(1)['Reference'].values])
a = (a[a['Date'].dt.month.values == v.tail(1)['Date'].dt.month.values])
a = (a[a['Date'].dt.year.values == v.tail(1)['Date'].dt.year.values])
# sum all days together and create the ones which are missing
for i, j in v.groupby(pd.Grouper(key='Date', freq='D')):
temp = j.tail(1).copy() # We need to get the last row
moves_dict[str(name)] = moves_dict[str(name)].append(j.tail(1))
if not a.empty:
# Drop last row (last day of the month) and add the one with the final stock
moves_dict[str(name)].drop(moves_dict[str(name)].index[len(moves_dict[str(name)])-1], inplace = True)
temp['final_stock'] = float(a['FINAL STOCK'])
moves_dict[str(name)] = moves_dict[str(name)].append(temp)
python performance pandas
New contributor
I started to work in a startup, but I'm the only data analyst, so I can't ask in my company, my problem is that in a lot of parts of my code I have to divide the dataframe, so I use the groupby to:
for name, group in mvtos_material_df.groupby('Referencia'):
# Work with the groups
But I think that working with for loops on pandas is wrong, and for some easy functions I saw that the idea is to use groupby().apply() but I have also the next code which I don't how to fix it, if not putting it all in a method, but seems a wrong idea.
moves_dict = {}
for name, group in mvtos_material_df.groupby('Reference'):
moves_dict[str(name)] = pd.DataFrame()
# for loop to fill moves_dict with the orders per day
for k, v in group.groupby(pd.Grouper(key='Date', freq='M')):
if not v.empty:
# comparing dates in inventory and movements to get the final stock
a = (inventory_df[inventory_df['REFERENCE'].values == v.tail(1)['Reference'].values])
a = (a[a['Date'].dt.month.values == v.tail(1)['Date'].dt.month.values])
a = (a[a['Date'].dt.year.values == v.tail(1)['Date'].dt.year.values])
# sum all days together and create the ones which are missing
for i, j in v.groupby(pd.Grouper(key='Date', freq='D')):
temp = j.tail(1).copy() # We need to get the last row
moves_dict[str(name)] = moves_dict[str(name)].append(j.tail(1))
if not a.empty:
# Drop last row (last day of the month) and add the one with the final stock
moves_dict[str(name)].drop(moves_dict[str(name)].index[len(moves_dict[str(name)])-1], inplace = True)
temp['final_stock'] = float(a['FINAL STOCK'])
moves_dict[str(name)] = moves_dict[str(name)].append(temp)
python performance pandas
python performance pandas
New contributor
New contributor
New contributor
asked 3 mins ago
set92
1
1
New contributor
New contributor
add a comment |
add a comment |
active
oldest
votes
Your Answer
StackExchange.ifUsing("editor", function () {
return StackExchange.using("mathjaxEditing", function () {
StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix) {
StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["\$", "\$"]]);
});
});
}, "mathjax-editing");
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: "196"
};
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',
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
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
});
}
});
set92 is a new contributor. Be nice, and check out our Code of Conduct.
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%2fcodereview.stackexchange.com%2fquestions%2f209801%2fhow-to-properly-use-groupby-on-pandas%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
active
oldest
votes
active
oldest
votes
active
oldest
votes
active
oldest
votes
set92 is a new contributor. Be nice, and check out our Code of Conduct.
set92 is a new contributor. Be nice, and check out our Code of Conduct.
set92 is a new contributor. Be nice, and check out our Code of Conduct.
set92 is a new contributor. Be nice, and check out our Code of Conduct.
Thanks for contributing an answer to Code Review Stack Exchange!
- 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.
Use MathJax to format equations. MathJax reference.
To learn more, see our tips on writing great answers.
Some of your past answers have not been well-received, and you're in danger of being blocked from answering.
Please pay close attention to the following guidance:
- 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%2fcodereview.stackexchange.com%2fquestions%2f209801%2fhow-to-properly-use-groupby-on-pandas%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