We Keep Coding

Complete a pandas data frame with values from other data frames

I have 3 data frames. I need to enrich the data from df with the data columns from df2 and df3 so that df ends up with the columns 'Code', 'Quantity', 'Payment', 'Date', 'Name', 'Size', 'Product','product_id', 'Sector'.
The codes that are in df and not in df2 OR df3, need to receive "unknown" for the string columns and "0" for the numeric dtype columns
import pandas as pd
data = {'Code': [356, 177, 395, 879, 952, 999],
'Quantity': [20, 21, 19, 18, 15, 10],
'Payment': [173.78, 253.79, 158.99, 400, 500, 500],
'Date': ['2022-06-01', '2022-09-01','2022-08-01','2022-07-03', '2022-06-09', '2022-06-09']
}
df = pd.DataFrame(data)
df['Date']= pd.to_datetime(df['Date'])
data2 = {'Code': [356, 177, 395, 893, 697, 689, 687],
'Name': ['John', 'Mary', 'Ann', 'Mike', 'Bill', 'Joana', 'Linda'],
'Product': ['RRR', 'RRT', 'NGF', 'TRA', 'FRT', 'RTW', 'POU'],
'product_id': [189, 188, 16, 36, 59, 75, 55],
'Size': [1, 1, 3, 4, 5, 4, 7],
}
df2 = pd.DataFrame(data2)
data3 = {'Code': [879, 356, 389, 395, 893, 697, 689, 978],
'Name': ['Mark', 'John', 'Marry', 'Ann', 'Mike', 'Bill', 'Joana', 'James'],
'Product': ['TTT', 'RRR', 'RRT', 'NGF', 'TRA', 'FRT', 'RTW', 'DTS'],
'product_id': [988, 189, 188, 16, 36, 59, 75, 66],
'Sector': ['rt' , 'dx', 'sx', 'da', 'sa','sd','ld', 'pc'],
}
df3 = pd.DataFrame(data3)
I was using the following code to obtain the unknown codes by comparing with df2, but now i have to compare with df3 also and also add the data from the columns ['Name', 'Size', 'Product','product_id', 'Sector'].
common = df2.merge(df,on=['Code'])
new_codes = df[(~df['Code'].isin(common['Code']))]

How to Iterate through an array of dictionaries to copy only relevant keys to new dictionary?

I want to iterate through a dictionary array like the following to only copy the 'symbol' and 'product_progress' keys and their corresponding values to new dictionary array.
[{'coin_name': 'Bitcoin', 'coin_id': 'bitcoin', 'symbol': 'btc', 'rank': 1, 'product_progress': 93, 'team': 100, 'token_fundamentals': 100, 'github_activity': 95, 'marketing': 5, 'partnership': 5, 'uniqueness': 5, 'total_score': 96, 'exchange_name': 'Bitfinex', 'exchange_link': 'https://www.bitfinex.com/t/BTCUSD', 'website': 'https://bitcoin.org/en/', 'twitter': 'https://twitter.com/Bitcoin', 'telegram': None, 'whitepaper': 'https://bitcoin.org/en/bitcoin-paper'}, {'coin_name': 'Ethereum', 'coin_id': 'ethereum', 'symbol': 'eth', 'rank': 2, 'product_progress': 87, 'team': 98, 'token_fundamentals': 97, 'github_activity': 100, 'marketing': 5, 'partnership': 5, 'uniqueness': 5, 'total_score': 94, 'exchange_name': 'Gemini', 'exchange_link': 'https://gemini.com/', 'website': 'https://www.ethereum.org/', 'twitter': 'https://twitter.com/ethereum', 'telegram': None, 'whitepaper': 'https://ethereum.org/en/whitepaper/'}] ...
The code I have so far is:
# need to iterate through list of dictionaries
for index in range(len(projectlist3)):
for key in projectlist3[index]:
d['symbol'] = projectlist3[index]['symbol']
d['token_fundamentals'] = projectlist3[index]['token_fundamentals']
print(d)
It's just saving the last entry rather than all of the entries {'symbol': 'eth', 'token_fundamentals': 97}

Given your data:
l = [{
'coin_name': 'Bitcoin',
'coin_id': 'bitcoin',
'symbol': 'btc',
'rank': 1,
'product_progress': 93,
'team': 100,
'token_fundamentals': 100,
'github_activity': 95,
'marketing': 5,
'partnership': 5,
'uniqueness': 5,
'total_score': 96,
'exchange_name': 'Bitfinex',
'exchange_link': 'https://www.bitfinex.com/t/BTCUSD',
'website': 'https://bitcoin.org/en/',
'twitter': 'https://twitter.com/Bitcoin',
'telegram': None,
'whitepaper': 'https://bitcoin.org/en/bitcoin-paper'
}, {
'coin_name': 'Ethereum',
'coin_id': 'ethereum',
'symbol': 'eth',
'rank': 2,
'product_progress': 87,
'team': 98,
'token_fundamentals': 97,
'github_activity': 100,
'marketing': 5,
'partnership': 5,
'uniqueness': 5,
'total_score': 94,
'exchange_name': 'Gemini',
'exchange_link': 'https://gemini.com/',
'website': 'https://www.ethereum.org/',
'twitter': 'https://twitter.com/ethereum',
'telegram': None,
'whitepaper': 'https://ethereum.org/en/whitepaper/'
}]
You can use listcomp
new_l = [{field: d[field] for field in ['symbol', 'token_fundamentals']}
for d in l]
which is better equivalent of this:
new_l = []
for d in l:
new_d = {}
for field in ['symbol', 'token_fundamentals']:
new_d[field] = d[field]
new_l.append(new_d)

Judging by what your writing into d you want to save a list of objects so this would work:
[{"symbol": i['symbol'], "token_fundamentals": i['token_fundamentals']} for i in d]
Result:
[{'symbol': 'btc', 'token_fundamentals': 100}, {'symbol': 'eth', 'token_fundamentals': 97}]

Reorder strings within object based on another data frame lookup

This is my first data frame (df1).
I have to reorder the elements in sequence column (of my df1) based on the minimum count from the second data frame (df2)
so my end results should be like this

I think the code below would do what you want. I've commented inline and put links for further reading...
I know it could be compressed into shorter code, but I wanted the steps to be clear.
import pandas as pd
from pprint import pprint
data1 = {'id': ['A1234', 'A2345'],
'Sequence': ['16 31 17', '51 59 43']}
df1 = pd.DataFrame(data1)
# I assumed the label en count columns are integers
data2 = {'label': [10, 11, 12, 13, 16, 17, 21, 24, 31, 43, 44, 51, 59, 60],
'count': [214, 128, 135, 37, 184, 68, 267, 264, 231, 13, 82, 100, 99, 92]}
df2 = pd.DataFrame(data2)
def seq_value_sort(seq_df, label_df):
new_sequence_list = []
for value in seq_df['Sequence'].values:
print(f'{"":-<40}') # prints a line
# convert string to list of integers
# https://www.geeksforgeeks.org/python-converting-all-strings-in-list-to-integers/
sequence = [int(i) for i in value.split()]
# generate an unsorted list of dict items based on Sequence
data = []
for index, row in label_df.T.iteritems():
if int(row['label']) in sequence:
data.append({'label': int(row['label']),
'count': int(row['count'])})
pprint(data)
# now sort the unsorted list based on key 'count'
# https://stackoverflow.com/a/73050/9267296
data = sorted(data, key=lambda k: k['count'])
pprint(data)
# list comprehension to make list of strings out
# of the list of dict
# https://stackoverflow.com/a/7271523/9267296
sequence_sorted = [ str(item['label']) for item in data ]
pprint(sequence_sorted)
# create the final sequence string from the list
new_sequence_list.append(' '.join(sequence_sorted))
# create return data
return_data = {'id': list(seq_df['id'].values),
'Sequence': new_sequence_list}
pprint(return_data)
# finally return a new df
return pd.DataFrame(return_data)
df3 = seq_value_sort(df1, df2)
print(f'{"":-<40}')
print(df3)
EDIT:
Forgot the output:
----------------------------------------
[{'count': 184, 'label': 16},
{'count': 68, 'label': 17},
{'count': 231, 'label': 31}]
[{'count': 68, 'label': 17},
{'count': 184, 'label': 16},
{'count': 231, 'label': 31}]
['17', '16', '31']
----------------------------------------
[{'count': 13, 'label': 43},
{'count': 100, 'label': 51},
{'count': 99, 'label': 59}]
[{'count': 13, 'label': 43},
{'count': 99, 'label': 59},
{'count': 100, 'label': 51}]
['43', '59', '51']
{'Sequence': ['17 16 31', '43 59 51'], 'id': ['A1234', 'A2345']}
----------------------------------------
id Sequence
0 A1234 17 16 31
1 A2345 43 59 51

Plotly: How to subset data by year, month and day using dropdown menus?

I am trying to plot three graphs (day, month, year) and give the user the option to pick which graph they want to see with a dropdown menu. When I do it for (day, month) it works perfectly (with month showing as the default graph), but when I add (year), then (day, month) don't show up (in this scenario, I want year to be the default graph).
This is the working code:
# Plot Day
temp_day = pd.DataFrame(df.day.value_counts())
temp_day.reset_index(inplace=True)
temp_day.columns = ['day', 'tweet_count']
temp_day.sort_values(by=['day'], inplace=True)
temp_day.reset_index(inplace=True, drop=True)
trace_day = go.Scatter(
x=temp_day.day.values,
y=temp_day.tweet_count.values,
text = [f"{humanize.naturaldate(day)}: {count} tweets" for day,count in zip(temp_day.day.values,temp_day.tweet_count.values)],
hoverinfo='text',
mode='lines',
line = {
'color': my_color,
'width': 1.2
},
visible=False,
name="Day"
)
# Plot Month
temp_month = pd.DataFrame(df.YYYYMM.value_counts())
temp_month.reset_index(inplace=True)
temp_month.columns = ['YYYYMM', 'tweet_count']
temp_month['YYYYMM'] = temp_month['YYYYMM'].dt.strftime('%Y-%m')
temp_month.sort_values(by=['YYYYMM'], inplace=True)
temp_month.reset_index(inplace=True, drop=True)
trace_month = go.Scatter(
x=temp_month.YYYYMM.values,
y=temp_month.tweet_count.values,
mode='lines',
line = {
'color': my_color,
'width': 1.2
},
visible=True,
name="Month"
)
# Menus
updatemenus = list([
dict(
active=0,
buttons=list([
dict(label = 'Month',
method = 'update',
args = [{'visible': [True, False]},
{'title': 'Number of Tweets per Month'}]),
dict(label = 'Day',
method = 'update',
args = [{'visible': [False, True]},
{'title': 'Number of Tweets per Day'}]),
]),
)
])
# Layout
layout = go.Layout(title="Number of Tweets -- Pick a scale",
updatemenus=updatemenus,
)
fig = go.Figure(data=[trace_month, trace_day], layout=layout)
iplot(fig)
This is the code that doesn't work and I can't figure out why:
# Plot Day
temp_day = pd.DataFrame(df.day.value_counts())
temp_day.reset_index(inplace=True)
temp_day.columns = ['day', 'tweet_count']
temp_day.sort_values(by=['day'], inplace=True)
temp_day.reset_index(inplace=True, drop=True)
trace_day = go.Scatter(
x=temp_day.day.values,
y=temp_day.tweet_count.values,
text = [f"{humanize.naturaldate(day)}: {count} tweets" for day,count in zip(temp_day.day.values,temp_day.tweet_count.values)],
hoverinfo='text',
mode='lines',
line = {
'color': my_color,
'width': 1.2
},
visible=False,
name="Day"
)
# Plot Month
temp_month = pd.DataFrame(df.YYYYMM.value_counts())
temp_month.reset_index(inplace=True)
temp_month.columns = ['YYYYMM', 'tweet_count']
temp_month['YYYYMM'] = temp_month['YYYYMM'].dt.strftime('%Y-%m')
temp_month.sort_values(by=['YYYYMM'], inplace=True)
temp_month.reset_index(inplace=True, drop=True)
trace_month = go.Scatter(
x=temp_month.YYYYMM.values,
y=temp_month.tweet_count.values,
mode='lines',
line = {
'color': my_color,
'width': 1.2
},
visible=False,
name="Month"
)
# Plot year
temp_year = pd.DataFrame(df.year.value_counts())
temp_year.reset_index(inplace=True)
temp_year.columns = ['year', 'tweet_count']
temp_year.sort_values(by=['year'], inplace=True)
temp_year.reset_index(inplace=True, drop=True)
trace_year = go.Scatter(
x=temp_year.year.values,
y=temp_year.tweet_count.values,
text = [f"Year {year}: {count:,.0f} tweets" for year,count in zip(temp_year.year.values,temp_year.tweet_count.values)],
hoverinfo='text',
mode='lines+markers',
line = {
'color': my_color,
'width': 1.2
},
visible=True,
name="Year"
)
# Menus
updatemenus = list([
dict(
active=0,
buttons=list([
dict(label = 'Year',
method = 'update',
args = [{'visible': [True, False, False]},
{'title': 'Number of Tweets per Month'}]),
dict(label = 'Month',
method = 'update',
args = [{'visible': [False, True, False]},
{'title': 'Number of Tweets per Month'}]),
dict(label = 'Day',
method = 'update',
args = [{'visible': [False, False, True]},
{'title': 'Number of Tweets per Day'}]),
]),
)
])
# Layout
layout = go.Layout(title="Number of Tweets -- Pick a scale",
updatemenus=updatemenus,
)
fig = go.Figure(data=[trace_year, trace_month, trace_day], layout=layout)
iplot(fig)
Here's the data:
# Year
Scatter({
'hoverinfo': 'text',
'line': {'color': '#ff00a7', 'width': 1.2},
'mode': 'lines+markers',
'name': 'Year',
'text': [Year 2011: 73 tweets, Year 2012: 562 tweets, Year 2013: 1,153 tweets,
Year 2014: 700 tweets, Year 2015: 2,104 tweets, Year 2016: 1,816
tweets, Year 2017: 1,691 tweets, Year 2018: 1,082 tweets, Year 2019:
914 tweets, Year 2020: 482 tweets],
'visible': False,
'x': array([2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020]),
'y': array([ 73, 562, 1153, 700, 2104, 1816, 1691, 1082, 914, 482])
})
# Month
Scatter({
'line': {'color': '#ff00a7', 'width': 1.2},
'mode': 'lines',
'name': 'Month',
'visible': False,
'x': array(['2011-06', '2011-07', '2011-08', '2011-09', '2011-10', '2011-11',
'2011-12', '2012-01', '2012-02', '2012-03', '2012-04', '2012-05',
'2012-06', '2012-07', '2012-08', '2012-09', '2012-10', '2012-11',
'2012-12', '2013-01', '2013-02', '2013-03', '2013-04', '2013-05',
'2013-06', '2013-07', '2013-08', '2013-09', '2013-10', '2013-11',
'2013-12', '2014-01', '2014-02', '2014-03', '2014-04', '2014-05',
'2014-06', '2014-07', '2014-08', '2014-09', '2014-10', '2014-11',
'2014-12', '2015-01', '2015-02', '2015-03', '2015-04', '2015-05',
'2015-06', '2015-07', '2015-08', '2015-09', '2015-10', '2015-11',
'2015-12', '2016-01', '2016-02', '2016-03', '2016-04', '2016-05',
'2016-06', '2016-07', '2016-08', '2016-09', '2016-10', '2016-11',
'2016-12', '2017-01', '2017-02', '2017-03', '2017-04', '2017-05',
'2017-06', '2017-07', '2017-08', '2017-09', '2017-10', '2017-11',
'2017-12', '2018-01', '2018-02', '2018-03', '2018-04', '2018-05',
'2018-06', '2018-07', '2018-08', '2018-09', '2018-10', '2018-11',
'2018-12', '2019-01', '2019-02', '2019-03', '2019-04', '2019-05',
'2019-06', '2019-08', '2019-09', '2019-10', '2019-11', '2019-12',
'2020-01', '2020-02', '2020-03', '2020-04', '2020-05', '2020-06'],
dtype=object),
'y': array([ 1, 1, 2, 8, 4, 20, 37, 79, 16, 13, 8, 12, 2, 5,
68, 139, 57, 64, 99, 182, 63, 60, 74, 128, 59, 109, 126, 86,
77, 112, 77, 78, 44, 32, 22, 33, 46, 61, 66, 109, 81, 78,
50, 140, 151, 297, 173, 225, 69, 119, 213, 177, 134, 217, 189, 255,
149, 114, 127, 154, 116, 110, 150, 184, 179, 117, 161, 48, 115, 147,
153, 199, 174, 195, 154, 162, 114, 140, 90, 156, 81, 107, 62, 64,
49, 128, 127, 60, 89, 115, 44, 58, 86, 65, 102, 93, 82, 78,
158, 65, 50, 77, 55, 71, 70, 105, 124, 57])
})
# Day
Scatter({
'hoverinfo': 'text',
'line': {'color': '#ff00a7', 'width': 1.2},
'mode': 'lines',
'name': 'Day',
'text': [Jun 04 2011: 1 tweets, Jul 17 2011: 1 tweets, Aug 11 2011: 1 tweets,
..., Jun 17: 4 tweets, Jun 18: 1 tweets, Jun 19: 3 tweets],
'visible': False,
'x': array([datetime.date(2011, 6, 4), datetime.date(2011, 7, 17),
datetime.date(2011, 8, 11), ..., datetime.date(2020, 6, 17),
datetime.date(2020, 6, 18), datetime.date(2020, 6, 19)], dtype=object),
'y': array([1, 1, 1, ..., 4, 1, 3])
})

It's hard to tell what works and what doesn't in your code snippets since you've provided a datasample that just does not work. However, I do have a suggestion for you that answers your question directly regarding:
I am trying to plot three graphs (day, month, year) and give the user
the option to pick which graph they want to see with a dropdown menu
The snippet below will let you chose between two datasets: year and month. And those are the exact datasets that you've provided. When you've got a working sample for days, that dataset can be easily included. And I'll happily do that for you when you're ready.
Anyway, the following plot will let you chose year and month using a dropdown menu. And don't worry if the design wrt line and marker style are not to your liking. That is simply included in the code as placeholders for you to change as you see fit.
Year
Month
DAY
(tba...)
Complete code
import plotly.graph_objects as go
import pandas as pd
df_y=pd.DataFrame({'x':[2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020],
'y':[ 73, 562, 1153, 700, 2104, 1816, 1691, 1082, 914, 482]})
df_m=pd.DataFrame({'x':['2011-06', '2011-07', '2011-08', '2011-09', '2011-10', '2011-11',
'2011-12', '2012-01', '2012-02', '2012-03', '2012-04', '2012-05',
'2012-06', '2012-07', '2012-08', '2012-09', '2012-10', '2012-11',
'2012-12', '2013-01', '2013-02', '2013-03', '2013-04', '2013-05',
'2013-06', '2013-07', '2013-08', '2013-09', '2013-10', '2013-11',
'2013-12', '2014-01', '2014-02', '2014-03', '2014-04', '2014-05',
'2014-06', '2014-07', '2014-08', '2014-09', '2014-10', '2014-11',
'2014-12', '2015-01', '2015-02', '2015-03', '2015-04', '2015-05',
'2015-06', '2015-07', '2015-08', '2015-09', '2015-10', '2015-11',
'2015-12', '2016-01', '2016-02', '2016-03', '2016-04', '2016-05',
'2016-06', '2016-07', '2016-08', '2016-09', '2016-10', '2016-11',
'2016-12', '2017-01', '2017-02', '2017-03', '2017-04', '2017-05',
'2017-06', '2017-07', '2017-08', '2017-09', '2017-10', '2017-11',
'2017-12', '2018-01', '2018-02', '2018-03', '2018-04', '2018-05',
'2018-06', '2018-07', '2018-08', '2018-09', '2018-10', '2018-11',
'2018-12', '2019-01', '2019-02', '2019-03', '2019-04', '2019-05',
'2019-06', '2019-08', '2019-09', '2019-10', '2019-11', '2019-12',
'2020-01', '2020-02', '2020-03', '2020-04', '2020-05', '2020-06'],
'y':[ 1, 1, 2, 8, 4, 20, 37, 79, 16, 13, 8, 12, 2, 5,
68, 139, 57, 64, 99, 182, 63, 60, 74, 128, 59, 109, 126, 86,
77, 112, 77, 78, 44, 32, 22, 33, 46, 61, 66, 109, 81, 78,
50, 140, 151, 297, 173, 225, 69, 119, 213, 177, 134, 217, 189, 255,
149, 114, 127, 154, 116, 110, 150, 184, 179, 117, 161, 48, 115, 147,
153, 199, 174, 195, 154, 162, 114, 140, 90, 156, 81, 107, 62, 64,
49, 128, 127, 60, 89, 115, 44, 58, 86, 65, 102, 93, 82, 78,
158, 65, 50, 77, 55, 71, 70, 105, 124, 57]})
# IMPROVEMENT 1
# INSERT ANOTHER DATAFRAME FOR DAYS HERE WITH THE SAME STRUCTURE AS ABOVE
# IMPROVEMENT 1
# INCLUDE THE DATAFRAME AS VALUE AND THE NAME df_d as key
# in the dict below:
dfc = {'year':df_y, 'month':df_m}
# set index
for df in dfc.keys():
dfc[df].set_index('x', inplace=True)
# plotly start
fig = go.Figure()
# menu setup
updatemenu= []
# buttons for menu 1, names
buttons=[]
# plotly start
fig = go.Figure()
# one trace for each column per dataframe: AI and RANDOM
for df in dfc.keys():
fig.add_trace(go.Scatter(x=dfc[df].index,
y=dfc[df]['y'],
visible=True,
#marker=dict(size=12, line=dict(width=2)),
#marker_symbol = 'diamond',
name=df
)
)
# some line settings for fun
lines = [dict(color='royalblue', width=2, dash='dot'), dict(color='firebrick', width=1, dash='dash')]
markers = [dict(size=12, line=dict(width=2)), dict(size=12, line=dict(width=2))]
# create traces for each color:
# build argVals for buttons and create buttons
for i, df in enumerate(dfc.keys()):
args_y = []
args_x = []
for col in dfc[df]:
args_y.append(dfc[df][col].values)
args_x.append(dfc[df].index)
argVals = [ {'y':args_y, 'x':args_x,
'marker':markers[i], 'line': lines[i]}]
buttons.append(dict(method='update',
label=df,
visible=True,
args=argVals))
updatemenu=[]
your_menu=dict()
updatemenu.append(your_menu)
updatemenu[0]['buttons']=buttons
updatemenu[0]['direction']='down'
updatemenu[0]['showactive']=True
fig.update_layout(showlegend=False, updatemenus=updatemenu)
fig.show()

Turns out, all axis need to be in the same format. This was solved by setting year inside the plot year to:
x=temp_year.day.astype('datetime64[Y]').astype(str).values
y=temp_year.tweet_count.values

Combine or merge dictionaries based on more than one key or multi keys

I've been searching all morning but most examples of merging was based on only one key, I couldn't find anything on multiple keys.
x = [
{'pid':111, 'sid':6, 'eid':123, 'x_qty':30},
{'pid':222, 'sid':56, 'eid':6212, 'x_qty':2}
]
y = [
{'pid':111, 'sid':6, 'eid':123, 'y_qty':123},
{'pid':333, 'sid':56, 'eid':6212, 'y_qty':112}
]
Values of pid=111, sid=6, eid=123 match in both x and y, then combine as one record. If they don't match, just bring it over as is.
FINAL RESULTS THAT I WANT:
z = [
{'pid': 111, 'sid': 6, 'eid': 123, 'x_qty': 30, 'y_qty': 123},
{'pid': 222, 'sid': 56, 'eid': 6212, 'x_qty': 2},
{'pid': 333, 'sid': 56, 'eid': 6212, 'y_qty': 112}
]

This is re-keying off a tuple:
>>> from operator import itemgetter
>>> from collections import defaultdict
>>> data = defaultdict(dict)
>>> f = itemgetter('pid', 'sid', 'eid')
>>> for d in [*x, *y]:
... data[f(d)].update(d)
...
>>> list(data.values())
[{'eid': 123, 'pid': 111, 'sid': 6, 'x_qty': 30, 'y_qty': 123},
{'eid': 6212, 'pid': 222, 'sid': 56, 'x_qty': 2},
{'eid': 6212, 'pid': 333, 'sid': 56, 'y_qty': 112}]

Here is an alternative using 3rd party library pandas, which accepts a list of dictionaries.
import pandas as pd
# merge data
merged = pd.DataFrame(x).merge(pd.DataFrame(y), how='outer')
# iterate, remove nan, convert to int
res = [s.dropna().astype(int).to_dict() for _, s in merged.iterrows()]
[{'eid': 123, 'pid': 111, 'sid': 6, 'x_qty': 30, 'y_qty': 123},
{'eid': 6212, 'pid': 222, 'sid': 56, 'x_qty': 2},
{'eid': 6212, 'pid': 333, 'sid': 56, 'y_qty': 112}]