I am trying to insert data from xlsx file into mysqdl table. I want to insert data in table and if there is a duplicate on primary keys, I want to update the existing data otherwise insert. I have written the script already but I realized it is too much work and using pandas it is quick. How can I achieve it in pandas?
#!/usr/bin/env python3
import pandas as pd
import sqlalchemy
engine_str = 'mysql+pymysql://admin:mypass#localhost/mydb'
engine = sqlalchemy.create_engine(engine_str, echo=False, encoding='utf-8')\
file_name = "tmp/results.xlsx"
df = pd.read_excel(file_name)
I can think of two options, but number 1 might be cleaner/faster:
1) Make SQL decide on the update/insert. Check this other question. You can iterate by rows of your 'df', from i=1 to n. Inside the loop for the insertion you can write something like:
query = """INSERT INTO table (id, name, age) VALUES(%s, %s, %s)
ON DUPLICATE KEY UPDATE name=%s, age=%s"""
engine.execute(query, (df.id[i], df.name[i], df.age[i], df.name[i], df.age[i]))
2) Define a python function that returns True or False when the record exists and then use it in your loop:
def check_existence(user_id):
query = "SELECT EXISTS (SELECT 1 FROM your_table where user_id_str = %s);"
return list(engine.execute(query, (user_id, ) ) )[0][0] == 1
You could iterate over rows and do this check before inserting
Please also check the solution in this question and this one too which might work in your case.
Pangres is the tool for this job.
Overview here:
https://pypi.org/project/pangres/
Use the function pangres.fix_psycopg2_bad_cols to "clean" the columns in the DataFrame.
Code/usage here:
https://github.com/ThibTrip/pangres/wiki
https://github.com/ThibTrip/pangres/wiki/Fix-bad-column-names-postgres
Example code:
# From: <https://github.com/ThibTrip/pangres/wiki/Fix-bad-column-names-postgres>
import pandas as pd
# fix bad col/index names with default replacements (empty string for '(', ')' and '%'):
df = pd.DataFrame({'test()':[0],
'foo()%':[0]}).set_index('test()')
print(df)
test() foo()%
0 0
# clean cols, index w/ no replacements
df_fixed = fix_psycopg2_bad_cols(df)
print(df_fixed)
test foo
0 0
# fix bad col/index names with custom replacements - you MUST provide replacements for '(', ')' and '%':
# reset df
df = pd.DataFrame({'test()':[0],
'foo()%':[0]}).set_index('test()')
# clean cols, index w/ user-specified replacements
df_fixed = fix_psycopg2_bad_cols(df, replacements={'%':'percent', '(':'', ')':''})
print(df_fixed)
test foopercent
0 0
Will only fix/correct some of the bad characters:
Replaces '%', '(' and ')' (characters that won't play nicely or even at all)
But, useful in that it handles cleanup and upsert.
(p.s., I know this post is over 4 years old, but still shows up in Google results when searching for "pangres upsert determine number inserts and updates" as the top SO result, dated May 13, 2020.)
When using Pandas no iteration is needed. Isn't that faster?
df = pd.read_csv(csv_file,sep=';',names=['column'])
df.to_sql('table', con=con, if_exists='append', index=False, chunksize=20000)
Related
Is there a way to sort data and drop duplicates using pure pyarrow tables? My goal is to retrieve the latest version of each ID based on the maximum update timestamp.
Some extra details: my datasets are normally structured into at least two versions:
historical
final
The historical dataset would include all updated items from a source so it is possible to have duplicates for a single ID for each change that happened to it (picture a Zendesk or ServiceNow ticket, for example, where a ticket can be updated many times)
I then read the historical dataset using filters, convert it into a pandas DF, sort the data, and then drop duplicates on some unique constraint columns.
dataset = ds.dataset(history, filesystem, partitioning)
table = dataset.to_table(filter=filter_expression, columns=columns)
df = table.to_pandas().sort_values(sort_columns, ascending=True).drop_duplicates(unique_constraint, keep="last")
table = pa.Table.from_pandas(df=df, schema=table.schema, preserve_index=False)
# ds.write_dataset(final, filesystem, partitioning)
# I tend to write the final dataset using the legacy dataset so I can make use of the partition_filename_cb - that way I can have one file per date_id. Our visualization tool connects to these files directly
# container/dataset/date_id=20210127/20210127.parquet
pq.write_to_dataset(final, filesystem, partition_cols=["date_id"], use_legacy_dataset=True, partition_filename_cb=lambda x: str(x[-1]).split(".")[0] + ".parquet")
It would be nice to cut out that conversion to pandas and then back to a table, if possible.
Edit March 2022: PyArrow is adding more functionalities, though this one isn't here yet. My approach now would be:
def drop_duplicates(table: pa.Table, column_name: str) -> pa.Table:
unique_values = pc.unique(table[column_name])
unique_indices = [pc.index(table[column_name], value).as_py() for value in unique_values]
mask = np.full((len(table)), False)
mask[unique_indices] = True
return table.filter(mask=mask)
//end edit
I saw your question because I had a similar one, and I solved it for my work (due to IP issues I can't post the whole code but I'll try to answer as well as I can. I've never done this before)
import pyarrow.compute as pc
import pyarrow as pa
import numpy as np
array = table.column(column_name)
dicts = {dct['values']: dct['counts'] for dct in pc.value_counts(array).to_pylist()}
for key, value in dicts.items():
# do stuff
I used the 'value_counts' to find the unique values and how many of them there are (https://arrow.apache.org/docs/python/generated/pyarrow.compute.value_counts.html). Then I iterated over those values. If the value was 1, I selected the row by using
mask = pa.array(np.array(array) == key)
row = table.filter(mask)
and if the count was more then 1 I selected either the first or last one by using numpy boolean arrays as a mask again.
After iterating it was just as simple as pa.concat_tables(tables)
warning: this is a slow process. If you need something quick&dirty, try the "Unique" option (also in the same link I provided).
edit/extra:: you can make it a bit faster/less memory intensive by keeping up a numpy array of boolean masks while iterating over the dictionary. then in the end you return a "table.filter(mask=boolean_mask)".
I don't know how to calculate the speed though...
edit2:
(sorry for the many edits. I've been doing a lot of refactoring and trying to get it to work faster.)
You can also try something like:
def drop_duplicates(table: pa.Table, col_name: str) ->pa.Table:
column_array = table.column(col_name)
mask_x = np.full((table.shape[0]), False)
_, mask_indices = np.unique(np.array(column_array), return_index=True)
mask_x[mask_indices] = True
return table.filter(mask=mask_x)
The following gives a good performance. About 2mins for a table with half billion rows. The reason I don't do combine_chunks(): there is a bug, arrow seems can not combine chunk arrays if there size are too large. See details: https://issues.apache.org/jira/browse/ARROW-10172?src=confmacro
a = [len(tb3['ID'].chunk(i)) for i in range(len(tb3['ID'].chunks))]
c = np.array([np.arange(x) for x in a])
a = ([0]+a)[:-1]
c = pa.chunked_array(c+np.cumsum(a))
tb3= tb3.set_column(tb3.shape[1], 'index', c)
selector = tb3.group_by(['ID']).aggregate([("index", "min")])
tb3 = tb3.filter(pc.is_in(tb3['index'], value_set=selector['index_min']))
I found duckdb can give better performance on group by. Change the last 2 lines above into the following will give 2X speedup:
import duckdb
duck = duckdb.connect()
sql = "select first(index) as idx from tb3 group by ID"
duck_res = duck.execute(sql).fetch_arrow_table()
tb3 = tb3.filter(pc.is_in(tb3['index'], value_set=duck_res['idx']))
I'd like to update a table with Django - something like this in raw SQL:
update tbl_name set name = 'foo' where name = 'bar'
My first result is something like this - but that's nasty, isn't it?
list = ModelClass.objects.filter(name = 'bar')
for obj in list:
obj.name = 'foo'
obj.save()
Is there a more elegant way?
Update:
Django 2.2 version now has a bulk_update.
Old answer:
Refer to the following django documentation section
Updating multiple objects at once
In short you should be able to use:
ModelClass.objects.filter(name='bar').update(name="foo")
You can also use F objects to do things like incrementing rows:
from django.db.models import F
Entry.objects.all().update(n_pingbacks=F('n_pingbacks') + 1)
See the documentation.
However, note that:
This won't use ModelClass.save method (so if you have some logic inside it won't be triggered).
No django signals will be emitted.
You can't perform an .update() on a sliced QuerySet, it must be on an original QuerySet so you'll need to lean on the .filter() and .exclude() methods.
Consider using django-bulk-update found here on GitHub.
Install: pip install django-bulk-update
Implement: (code taken directly from projects ReadMe file)
from bulk_update.helper import bulk_update
random_names = ['Walter', 'The Dude', 'Donny', 'Jesus']
people = Person.objects.all()
for person in people:
r = random.randrange(4)
person.name = random_names[r]
bulk_update(people) # updates all columns using the default db
Update: As Marc points out in the comments this is not suitable for updating thousands of rows at once. Though it is suitable for smaller batches 10's to 100's. The size of the batch that is right for you depends on your CPU and query complexity. This tool is more like a wheel barrow than a dump truck.
Django 2.2 version now has a bulk_update method (release notes).
https://docs.djangoproject.com/en/stable/ref/models/querysets/#bulk-update
Example:
# get a pk: record dictionary of existing records
updates = YourModel.objects.filter(...).in_bulk()
....
# do something with the updates dict
....
if hasattr(YourModel.objects, 'bulk_update') and updates:
# Use the new method
YourModel.objects.bulk_update(updates.values(), [list the fields to update], batch_size=100)
else:
# The old & slow way
with transaction.atomic():
for obj in updates.values():
obj.save(update_fields=[list the fields to update])
If you want to set the same value on a collection of rows, you can use the update() method combined with any query term to update all rows in one query:
some_list = ModelClass.objects.filter(some condition).values('id')
ModelClass.objects.filter(pk__in=some_list).update(foo=bar)
If you want to update a collection of rows with different values depending on some condition, you can in best case batch the updates according to values. Let's say you have 1000 rows where you want to set a column to one of X values, then you could prepare the batches beforehand and then only run X update-queries (each essentially having the form of the first example above) + the initial SELECT-query.
If every row requires a unique value there is no way to avoid one query per update. Perhaps look into other architectures like CQRS/Event sourcing if you need performance in this latter case.
Here is a useful content which i found in internet regarding the above question
https://www.sankalpjonna.com/learn-django/running-a-bulk-update-with-django
The inefficient way
model_qs= ModelClass.objects.filter(name = 'bar')
for obj in model_qs:
obj.name = 'foo'
obj.save()
The efficient way
ModelClass.objects.filter(name = 'bar').update(name="foo") # for single value 'foo' or add loop
Using bulk_update
update_list = []
model_qs= ModelClass.objects.filter(name = 'bar')
for model_obj in model_qs:
model_obj.name = "foo" # Or what ever the value is for simplicty im providing foo only
update_list.append(model_obj)
ModelClass.objects.bulk_update(update_list,['name'])
Using an atomic transaction
from django.db import transaction
with transaction.atomic():
model_qs = ModelClass.objects.filter(name = 'bar')
for obj in model_qs:
ModelClass.objects.filter(name = 'bar').update(name="foo")
Any Up Votes ? Thanks in advance : Thank you for keep an attention ;)
To update with same value we can simply use this
ModelClass.objects.filter(name = 'bar').update(name='foo')
To update with different values
ob_list = ModelClass.objects.filter(name = 'bar')
obj_to_be_update = []
for obj in obj_list:
obj.name = "Dear "+obj.name
obj_to_be_update.append(obj)
ModelClass.objects.bulk_update(obj_to_be_update, ['name'], batch_size=1000)
It won't trigger save signal every time instead we keep all the objects to be updated on the list and trigger update signal at once.
IT returns number of objects are updated in table.
update_counts = ModelClass.objects.filter(name='bar').update(name="foo")
You can refer this link to get more information on bulk update and create.
Bulk update and Create
Versions: Django 1.10 and Postgres 9.6
I'm trying to modify a nested JSONField's key in place without a roundtrip to Python. Reason is to avoid race conditions and multiple queries overwriting the same field with different update.
I tried to chain the methods in the hope that Django would make a single query but it's being logged as two:
Original field value (demo only, real data is more complex):
from exampleapp.models import AdhocTask
record = AdhocTask.objects.get(id=1)
print(record.log)
> {'demo_key': 'original'}
Query:
from django.db.models import F
from django.db.models.expressions import RawSQL
(AdhocTask.objects.filter(id=25)
.annotate(temp=RawSQL(
# `jsonb_set` gets current json value of `log` field,
# take a the nominated key ("demo key" in this example)
# and replaces the value with the json provided ("new value")
# Raw sql is wrapped in triple quotes to avoid escaping each quote
"""jsonb_set(log, '{"demo_key"}','"new value"', false)""",[]))
# Finally, get the temp field and overwrite the original JSONField
.update(log=F('temp’))
)
Query history (shows this as two separate queries):
from django.db import connection
print(connection.queries)
> {'sql': 'SELECT "exampleapp_adhoctask"."id", "exampleapp_adhoctask"."description", "exampleapp_adhoctask"."log" FROM "exampleapp_adhoctask" WHERE "exampleapp_adhoctask"."id" = 1', 'time': '0.001'},
> {'sql': 'UPDATE "exampleapp_adhoctask" SET "log" = (jsonb_set(log, \'{"demo_key"}\',\'"new value"\', false)) WHERE "exampleapp_adhoctask"."id" = 1', 'time': '0.001'}]
It would be much nicer without RawSQL.
Here's how to do it:
from django.db.models.expressions import Func
class ReplaceValue(Func):
function = 'jsonb_set'
template = "%(function)s(%(expressions)s, '{\"%(keyname)s\"}','\"%(new_value)s\"', %(create_missing)s)"
arity = 1
def __init__(
self, expression: str, keyname: str, new_value: str,
create_missing: bool=False, **extra,
):
super().__init__(
expression,
keyname=keyname,
new_value=new_value,
create_missing='true' if create_missing else 'false',
**extra,
)
AdhocTask.objects.filter(id=25) \
.update(log=ReplaceValue(
'log',
keyname='demo_key',
new_value='another value',
create_missing=False,
)
ReplaceValue.template is the same as your raw SQL statement, just parametrized.
(jsonb_set(log, \'{"demo_key"}\',\'"another value"\', false)) from your query is now jsonb_set("exampleapp.adhoctask"."log", \'{"demo_key"}\',\'"another value"\', false). The parentheses are gone (you can get them back by adding it to the template) and log is referenced in a different way.
Anyone interested in more details regarding jsonb_set should have a look at table 9-45 in postgres' documentation: https://www.postgresql.org/docs/9.6/static/functions-json.html#FUNCTIONS-JSON-PROCESSING-TABLE
Rubber duck debugging at its best - in writing the question, I've realised the solution. Leaving the answer here in hope of helping someone in future:
Looking at the queries, I realised that the RawSQL was actually being deferred until query two, so all I was doing was storing the RawSQL as a subquery for later execution.
Solution:
Skip the annotate step altogether and use the RawSQL expression straight into the .update() call. Allows you to dynamically update PostgresQL jsonb sub-keys on the database server without overwriting the whole field:
(AdhocTask.objects.filter(id=25)
.update(log=RawSQL(
"""jsonb_set(log, '{"demo_key"}','"another value"', false)""",[])
)
)
> 1 # Success
print(connection.queries)
> {'sql': 'UPDATE "exampleapp_adhoctask" SET "log" = (jsonb_set(log, \'{"demo_key"}\',\'"another value"\', false)) WHERE "exampleapp_adhoctask"."id" = 1', 'time': '0.001'}]
print(AdhocTask.objects.get(id=1).log)
> {'demo_key': 'another value'}
I'm executing a SQL "SELECT" query on a MySQL database via python, using PyMySQL as the interface. Below is the excerpt of the code which performs the task:
try:
with self.connection.cursor() as cursor:
sql = "SELECT `symbol`,`clordid`,`side`,`status` FROM " + tablename + " WHERE `tradedate` >= %s AND (`status` =%s OR `status`=%s)"
cursor.execute(sql,(str(begindate.date()),'I','T'))
a = cursor.fetchall()
The query executes just fine. The problem is that the column ordering of the results doesn't match the order specified within the query. If I run add the following code:
for b in a:
print b.values()
The values in variable 'b' appear in the following order:
'status', 'symbol', 'side', 'clordid'
Moreover, it doesn't matter which order is specified by me- the results always appear in this order. Is there any way to fix this? Thanks in advance!
In testing I found the selected answer (convert dict to OrderedDict) to be unreliable in preserving query result column order.
#vaultah's answer in a similar question suggests using pymysql.cursors.DictCursorMixin:
class OrderedDictCursor(DictCursorMixin, Cursor):
dict_type = OrderedDict
...to create a cursor that remembers the correct column order:
cursor = conn.cursor(OrderedDictCursor)
Then get your results like normal:
results = cursor.fetchall()
for row in results:
print row # properly ordered columns
I prefer this approach better because it's stable, requires less code, and handles ordering at the appropriate level (as the columns are read).
I amolst sure you need collections.OrderedDict, as each table row is a dict where keys stays for columns:
# python 2.7
import pymysql.cursors
from collections import OrderedDict
# ...
results = cursor.fetchall()
for i in results:
print OrderedDict(sorted(i.items(), key=lambda t: t[0]))
Also, based on your code snippet b.values() sounds like SQL ORDER BY col_name ASC|DESC. On this case SQL should be work pretty well.
Since you liked that solutuion
Here is an approach:
with self.connection.cursor() as cursor:
sql = "SELECT `symbol`,`clordid`,`side`,`status` FROM " + tablename + " WHERE `tradedate` >= %s AND (`status` =%s OR `status`=%s)"
cursor.execute(sql,(str(begindate.date()),'I','T'))
a = cursor.fetchall()
for b in a:
print "%s, %s, %s, %s" % (b["symbol"], b["clordid"], b["side"], b["status"])
I am not sure, if I should post this answer or to flag your OP to be closed as a duplicate.
Very similar to this question MySQL Dynamic Query Statement in Python
However what I am looking to do instead of two lists is to use a dictionary
Let's say i have this dictionary
instance_insert = {
# sql column variable value
'instance_id' : 'instnace.id',
'customer_id' : 'customer.id',
'os' : 'instance.platform',
}
And I want to populate a mysql database with an insert statement using sql column as the sql column name and the variable name as the variable that will hold the value that is to be inserted into the mysql table.
Kind of lost because I don't understand exactly what this statement does, but was pulled from the question that I posted where he was using two lists to do what he wanted.
sql = "INSERT INTO instance_info_test VALUES (%s);" % ', '.join('?' for _ in instance_insert)
cur.execute (sql, instance_insert)
Also I would like it to be dynamic in the sense that I can add/remove columns to the dictionary
Before you post, you might want to try searching for something more specific to your question. For instance, when I Googled "python mysqldb insert dictionary", I found a good answer on the first page, at http://mail.python.org/pipermail/tutor/2010-December/080701.html. Relevant part:
Here's what I came up with when I tried to make a generalized version
of the above:
def add_row(cursor, tablename, rowdict):
# XXX tablename not sanitized
# XXX test for allowed keys is case-sensitive
# filter out keys that are not column names
cursor.execute("describe %s" % tablename)
allowed_keys = set(row[0] for row in cursor.fetchall())
keys = allowed_keys.intersection(rowdict)
if len(rowdict) > len(keys):
unknown_keys = set(rowdict) - allowed_keys
print >> sys.stderr, "skipping keys:", ", ".join(unknown_keys)
columns = ", ".join(keys)
values_template = ", ".join(["%s"] * len(keys))
sql = "insert into %s (%s) values (%s)" % (
tablename, columns, values_template)
values = tuple(rowdict[key] for key in keys)
cursor.execute(sql, values)
filename = ...
tablename = ...
db = MySQLdb.connect(...)
cursor = db.cursor()
with open(filename) as instream:
row = json.load(instream)
add_row(cursor, tablename, row)
Peter
If you know your inputs will always be valid (table name is valid, columns are present in the table), and you're not importing from a JSON file as the example is, you can simplify this function. But it'll accomplish what you want to accomplish. While it may initially seem like DictCursor would be helpful, it looks like DictCursor is useful for returning a dictionary of values, but it can't execute from a dict.