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Updating dictionaries based on huge list of dicts

I have a huge(around 350k elements) list of dictionaries:
lst = [
{'data': 'xxx', 'id': 1456},
{'data': 'yyy', 'id': 24234},
{'data': 'zzz', 'id': 3222},
{'data': 'foo', 'id': 1789},
]
On the other hand I receive dictionaries(around 550k) one by one with missing value(not every dict is missing this value) which I need to update from:
example_dict = {'key': 'x', 'key2': 'y', 'id': 1456, 'data': None}
To:
example_dict = {'key': 'x', 'key2': 'y', 'id': 1456, 'data': 'xxx'}
And I need to take each dict and search withing the list for matching 'id' and update the 'data'. Doing it this way takes ages to process:
if example_dict['data'] is None:
for row in lst:
if row['id'] == example_dict['id']:
example_dict['data'] = row['data']
Is there a way to build a structured chunked data divided to in e.g. 10k values and tell the incoming dict in which chunk to search for the 'id'? Or any other way to to optimize that? Any help is appreciated, take care.

Use a dict instead of searching linearly through the list.
The first important optimization is to remove that linear search through lst, by building a dict indexed on id pointing to the rows
For example, this will be a lot faster than your code, if you have enough RAM to hold all the rows in memory:
row_dict = {row['id']: row for row in lst}
if example_dict['data'] is None:
if example_dict['id'] in row_dict:
example_dict['data'] = row_dict[example_dict['id']]['data']
This improvement will be relevant for you whether you process the rows by chunks of 10k or all at once, since dictionary lookup time is constant instead of linear in the size of lst.
Make your own chunking process
Next you ask "Is there a way to build a structured chunked data divided...". Yes, absolutely. If the data is too big to fit in memory, write a first pass function that divides the input based on id into several temporary files. They could be based on the last two digits of ID if order is irrelevant, or on ranges of ids if you prefer. Do that for both the list of rows, and the dictionaries you receive, and then process each list/dict file pairs on the same ids one at a time, with code like above.
If you have to preserve the order in which you receive the dictionaries, though, this approach will be more difficult to implement.

Some preprocessing of lst list might help a lot. E.g. transform that list of dicts into dictionary, where id would be a key.
To be precise transform lst into such structure:
lst = {
'1456': 'xxx',
'24234': 'yyy',
'3222': 'zzz',
...
}
Then when trying to check your data attributes in example_dict, just access straight to id key in lst as follows:
if example_dict['data'] is None:
example_dict['data'] = lst.get(example_dict['id'])
It should reduce the time complexity from something as quadratic complexity (n*n) to linear complexity (n).

Try creating creating a hash table (in Python, a dict) from lst to speed up the lookup based on 'id':
lst = [
{'data': 'xxx', 'id': 1456},
{'data': 'yyy', 'id': 24234},
{'data': 'zzz', 'id': 3222},
{'data': 'foo', 'id': 1789},
]
example_dict = {'key': 'x', 'key2': 'y', 'id': 1456, 'data': None}
dct ={row['id'] : row for row in lst}
if example_dict['data'] is None:
example_dict['data'] = dct[example_dict['id']]['data']
print(example_dict)
Sample output:
{'key': 'x', 'key2': 'y', 'id': 1456, 'data': 'xxx'}

create a pandas dataframe from python list containing tuple with nested dictionary

I have wrestled with this for a few days now, but can't figure it out.
I'm trying to create a dataframe "account_activity" from the results of an api get.
i make an api call and print it out.
account_activities = api.get_activities()
print(account_activities)
returns:
[AccountActivity({ 'activity_type': 'FILL',
'cum_qty': '100',
'id': '20211111105648607::a0ef3f04-ff00-4b8e-834d-54737d89c332',
'leaves_qty': '0',
'order_id': '32c9a40e-e6d2-4c7c-8949-a39ad32b535f',
'order_status': 'filled',
'price': '187.09',
'qty': '56',
'side': 'sell',
'symbol': 'U',
'transaction_time': '2021-11-11T15:56:48.607222Z',
'type': 'fill'})]
How do I create a dataframe "account_activity" where the keys are the column headers and the index is the transaction_time is the row index with values in the rows?

Assuming j is te JSON from your AccountActivity object:
df = pd.DataFrame(j, index=['']).set_index('transaction_time',drop=True)
How you get the JSON depends on the APIs you're using. Perhaps
j = account_activities[0].__dict__
will work?

How to access the dictionary key values inside a list which is a column in a data frame

I have a data frame with multiple columns like key_id, name, score, outcome & category_reasons.
The category_reasons columns holds value as below for each of the key_id. For some of the key_id it will have only one category & value for other it will have multiple category & value as below
How can I create a new column such that it includes only the category values for the category field as a list in a new columnSample Input/Output category_reasons1.
Please see the embedded like to view the Sample Input & Output required.
Could anyone help me on how to resolve this issue?
[{'category': 'A', 'value': ['12']},
{'category': 'B', 'value': ['13a']},
{'category': 'C', 'value': ['14c']}]
Sample Input/output

To get keys:
You can just create a new column as :
df["new_category_reasons"] = [','.join(l.keys()) for l in [OrderedDict(d) for d in df["category_reasons"]]]
or if you don't want to create a new column , just assign the existing column :
df["category_reasons"] = [','.join(l.keys()) for l in [OrderedDict(d) for d in df["category_reasons"]]]
Get OrderedDict from each dictionary in the column 'category_reasons' and join the keys with "," to get the comma separated string of keys.
You need OrderedDict in order to preserve the order of keys and then get the first key. Otherwise you may get value as the first key in d.keys() sometimes as you know keys() is a set which is unordered by nature.
Example :
import pandas as pd
from pandas import Series,DataFrame
data = [[1,[{'categoryA': 'A', 'value': ['12']},
{'categoryB': 'B', 'value': ['13a']},
{'categoryC': 'C', 'value': ['14c']}]], [2,[{'categoryA':'A', 'value':['12']}, {'categoryB':'B', 'value':['13a']}]]]
df = pd.DataFrame(data, columns = ["key_id","category_reasons"])
from collections import OrderedDict
df['new_category_reasons'] = [','.join(l.keys()) for l in [OrderedDict(d) for d in df["category_reasons"]]]
Output:
key_id category_reasons new_category_reasons
0 1 [{'categoryA': 'A', 'value': ['12']}, {'catego... categoryA,categoryB,categoryC
1 2 [{'categoryA': 'A', 'value': ['12']}, {'catego... categoryA,categoryB
To get values:
To get the values e.g. [A,B,C] instead, you could do the following:
You could define a function to extract values from the list of dictionaries that is present in each 'category_reason' like:
def get_category_values(category_list):
l = []
for d in category_list:
od = OrderedDict(d)
l.append(od[list(od.keys())[0]])
return l
Use this function with a list comprehension to get the new column 'category_reason1' like:
df['category_reason1'] = [get_category_values(category_list) for category_list in df['category_reasons']]
Output :
key_id category_reasons category_reason1
0 1 [{'categoryA': 'A', 'value': ['12']}, {'catego... [A, B, C]
1 2 [{'categoryA': 'A', 'value': ['12']}, {'catego... [A, B]

Fill pandas dataframe within a for loop

I am working with Amazon Rekognition to do some image analysis.
With a symple Python script, I get - at every iteration - a response of this type:
(example for the image of a cat)
{'Labels':
[{'Name': 'Pet', 'Confidence': 96.146484375, 'Instances': [],
'Parents': [{'Name': 'Animal'}]}, {'Name': 'Mammal', 'Confidence': 96.146484375,
'Instances': [], 'Parents': [{'Name': 'Animal'}]},
{'Name': 'Cat', 'Confidence': 96.146484375.....
I got all the attributes I need in a list, that looks like this:
[Pet, Mammal, Cat, Animal, Manx, Abyssinian, Furniture, Kitten, Couch]
Now, I would like to create a dataframe where the elements in the list above appear as columns and the rows take values 0 or 1.
I created a dictionary in which I add the elements in the list, so I get {'Cat': 1}, then I go to add it to the dataframe and I get the following error:
TypeError: Index(...) must be called with a collection of some kind, 'Cat' was passed.
Not only that, but I don't even seem able to add to the same dataframe the information from different images. For example, if I only insert the data in the dataframe (as rows, not columns), I get a series with n rows with the n elements (identified by Amazon Rekognition) of only the last image, i.e. I start from an empty dataframe at each iteration.
The result I would like to get is something like:
Image Human Animal Flowers etc...
Pic1 1 0 0
Pic2 0 0 1
Pic3 1 1 0
For reference, this is the code I am using now (I should add that I am working on a software called KNIME, but this is just Python):
from pandas import DataFrame
import pandas as pd
import boto3
fileName=flow_variables['Path_Arr[1]'] #This is just to tell Amazon the name of the image
bucket= 'mybucket'
client=boto3.client('rekognition', region_name = 'us-east-2')
response = client.detect_labels(Image={'S3Object':
{'Bucket':bucket,'Name':fileName}})
data = [str(response)] # This is what I inserted in the first cell of this question
d= {}
for key, value in response.items():
for el in value:
if isinstance(el,dict):
for k, v in el.items():
if k == "Name":
d[v] = 1
print(d)
df = pd.DataFrame(d, ignore_index=True)
print(df)
output_table = df
I am definitely getting it all wrong both in the for loop and when adding things to my dataframe, but nothing really seems to work!
Sorry for the super long question, hope it was clear! Any ideas?

I do not know if this answers your question completely, because i do not know, what you data can look like, but it's a good step that should help you, i think. I added the same data multiple time, but the way should be clear.
import pandas as pd
response = {'Labels': [{'Name': 'Pet', 'Confidence': 96.146484375, 'Instances': [], 'Parents': [{'Name': 'Animal'}]},
{'Name': 'Cat', 'Confidence': 96.146484375, 'Instances': [{'BoundingBox':
{'Width': 0.6686800122261047,
'Height': 0.9005332589149475,
'Left': 0.27255237102508545,
'Top': 0.03728689253330231},
'Confidence': 96.146484375}],
'Parents': [{'Name': 'Pet'}]
}]}
def handle_new_data(repsonse_data: dict, image_name: str) -> pd.DataFrame:
d = {"Image": image_name}
result = pd.DataFrame()
for key, value in repsonse_data.items():
for el in value:
if isinstance(el, dict):
for k, v in el.items():
if k == "Name":
d[v] = 1
result = result.append(d, ignore_index=True)
return result
df_all = pd.DataFrame()
df_all = df_all.append(handle_new_data(response, "image1"))
df_all = df_all.append(handle_new_data(response, "image2"))
df_all = df_all.append(handle_new_data(response, "image3"))
df_all = df_all.append(handle_new_data(response, "image4"))
df_all.reset_index(inplace=True)
print(df_all)

Updating/updating a data table using python

I would like some advice on how to update/insert new data into an already existing data table using Python/Databricks:
# Inserting and updating already existing data
# Original data
import pandas as pd
source_data = {'Customer Number': ['1', '2', '3'],
'Colour': ['Red', 'Blue', 'Green'],
'Flow': ['Good', 'Bad', "Good"]
}
df1 = pd.DataFrame (source_data, columns = ['Customer Number','Colour', 'Flow'])
print(df1)
# New data
new_data = {'Customer Number': ['1', '4',],
'Colour': ['Blue', 'Blue'],
'Flow': ['Bad', 'Bad']
}
df2 = pd.DataFrame (new_data, columns = ['Customer Number','Colour', 'Flow'])
print(df2)
# What the updated table will look like
updated_data = {'Customer Number': ['1', '2', '3', '4',],
'Colour': ['Blue', 'Blue', 'Green', 'Blue',],
'Flow': ['Bad', 'Bad', "Good", 'Bad']
}
df3 = pd.DataFrame (updated_data, columns = ['Customer Number','Colour', 'Flow'])
print(df3)
What you can see here is that the original data has three customers. I then get 'new_data' which contains an update of customer 1's data and new data for 'customer 4', who was not already in the original data. Then if you look at 'updated_data' you can see what the final data should look like. Here 'Customer 1's data has been updated and customer 4s data has been inserted.
Does anyone know where I should start with this? Which module I could use?
I’m not expecting someone to solve this in terms of developing, just need a nudge in the right direction.
Edit: the data source is .txt or CSV, the output is JSON, but as I load the data to Cosmos DB it’ll automatically convert so don’t worry too much about that.
Thanks

Current data frame structure and 'pd.update'
With some preparation, you can use the pandas 'update' function.
First, the data frames must be indexed (this is often useful anyway).
Second, the source data frame must be extended by the new indices with dummy/NaN data so that it can be updated.
# set indices of original data frames
col = 'Customer Number'
df1.set_index(col, inplace=True)
df2.set_index(col, inplace=True)
df3.set_index(col, inplace=True)
# extend source data frame by new customer indices
df4 = df1.copy().reindex(index=df1.index.union(df2.index))
# update data
df4.update(df2)
# verify that new approach yields correct results
assert all(df3 == df4)
Current data frame structure and 'pd.concat'
A slightly easier approach joins the data frames and removes duplicate
rows (and sorts by index if wanted). However, the temporary concatenation requires
more memory which may limit the size of the data frames.
df5 = pd.concat([df1, df2])
df5 = df5.loc[~df5.index.duplicated(keep='last')].sort_index()
assert all(df3 == df5)
Alternative data structure
Given that 'Customer Number' is the crucial attribute of your data,
you may also consider restructuring your original dictionaries like that:
{'1': ['Red', 'Good'], '2': ['Blue', 'Bad'], '3': ['Green', 'Good']}
Then updating your data simply corresponds to (re)setting the key of the source data with the new data. Typically, working directly on dictionaries is faster than using data frames.
# define function to restructure data, for demonstration purposes only
def restructure(data):
# transpose original data
# https://stackoverflow.com/a/6473724/5350621
vals = data.values()
rows = list(map(list, zip(*vals)))
# create new restructured dictionary with customers as keys
restructured = dict()
for row in rows:
restructured[row[0]] = row[1:]
return restructured
# restructure data
source_restructured = restructure(source_data)
new_restructured = restructure(new_data)
# simply (re)set new keys
final_restructured = source_restructured.copy()
for key, val in new_restructured.items():
final_restructured[key] = val
# convert to data frame and check results
df6 = pd.DataFrame(final_restructured, index=['Colour', 'Flow']).T
assert all(df3 == df6)
PS: When setting 'df1 = pd.DataFrame(source_data, columns=[...])' you do not need the 'columns' argument because your dictionaries are nicely named and the keys are automatically taken as column names.

You can use set intersection to find the Customer Numbers to update and set difference to find new Customer Number to add.
Then you can first update the initial data frame rows iterating through the intersection of Costumer Number and then merge the initial data frame only with the new rows of the data frame with the new values.
# same name column for clarity
cn = 'Customer Number'
# convert Consumer Number values into integer to use set
CusNum_df1 = [int(x) for x in df1[cn].values]
CusNum_df2 = [int(x) for x in df2[cn].values]
# find Customer Numbers to update and to add
CusNum_to_update = list(set(CusNum_df1).intersection(set(CusNum_df2)))
CusNum_to_add = list(set(CusNum_df2) - set(CusNum_df1))
# update rows in initial data frame
for num in CusNum_to_update:
index_initial = df1.loc[df1[cn]==str(num)].index[0]
index_new = df2.loc[df2[cn]==str(num)].index[0]
for col in df1.columns:
df1.at[index_initial,col]= df2.loc[index_new,col]
# concatenate new rows to initial data frame
for num in CusNum_to_add:
df1 = pd.concat([df1, df2.loc[df2[cn]==str(num)]]).reset_index(drop=True)
out:
Customer Number Colour Flow
0 1 Blue Bad
1 2 Blue Bad
2 3 Green Good
3 4 Blue Bad

There are many ways, but in terms of readability, I would prefer to do this.
import pandas as pd
dict_source = {'Customer Number': ['1', '2', '3'],
'Colour': ['Red', 'Blue', 'Green'],
'Flow': ['Good', 'Bad', "Good"]
}
df_origin = pd.DataFrame.from_dict(dict_source)
dict_new = {'Customer Number': ['1', '4', ],
'Colour': ['Blue', 'Blue'],
'Flow': ['Bad', 'Bad']
}
df_new = pd.DataFrame.from_dict(dict_new)
df_result = df_origin.copy()
df_result.set_index(['Customer Number', ], inplace=True)
df_new.set_index(['Customer Number', ], inplace=True)
df_result.update(df_new) # update number 1
# handle number 4
df_result.reset_index(['Customer Number', ], inplace=True)
df_new.reset_index(['Customer Number', ], inplace=True)
df_result = df_result.merge(df_new, on=list(df_result), how='outer')
print(df_result)
Customer Number Colour Flow
0 1 Blue Bad
1 2 Blue Bad
2 3 Green Good
3 4 Blue Bad

You can use 'Customer Number' as index and use update method:
import pandas as pd
source_data = {'Customer Number': ['1', '2', '3'],
'Colour': ['Red', 'Blue', 'Green'],
'Flow': ['Good', 'Bad', "Good"]
}
df1 = pd.DataFrame (source_data, index=source_data['Customer Number'], columns=['Colour', 'Flow'])
print(df1)
# New data
new_data = {'Customer Number': ['1', '4',],
'Colour': ['Blue', 'Blue'],
'Flow': ['Bad', 'Bad']
}
df2 = pd.DataFrame (new_data, index=new_data['Customer Number'], columns=['Colour', 'Flow'])
print(df2)
df3 = df1.reindex(index=df1.index.union(df2.index))
df3.update(df2)
print(df3)
Colour Flow
1 Blue Bad
2 Blue Bad
3 Green Good
4 Blue Bad