Can't get pymongo to connect to mongodb in jupyter notebook to run CRUD python file?
Question
I am running into the error that the authentication is not working for my project. I have tried a few different solutions that I found online and it still won't authenticate the user for me. I tried to write it in different ways and found similar issues on stackoverflow that had working solutions but the solutions did not work for me. I have pasted a couple of the versions that I have tried below.
The error I am currently getting is:
'Collection' object is not callable. If you meant to call the 'authenticate' method on a 'Database' object it is failing because no such method exists.
AnimalShelterTest.py code
import pymongo
from pymongo import MongoClient
from bson.objectid import ObjectId
class AnimalShelter(object):
""" CRUD operations for Animal collection in MongoDB """
def __init__(self, username, password):
#Initializing the MongoClient. This helps to access the MongoDB databases and collections.
connection = pymongo.MongoClient("localhost:27017")
db = connection['AAC']
db.authenticate(username, password)
#Complete this create method to implement the C in CRUD.
def create(self, data):
if data is not None:
insert = self.database.animals.insert(data) #data should be dictionary
else:
raise Exception("Nothing to save, because data parameter is empty")
#Create method to implement the R in CRUD.
def read(self, searchData):
if searchData:
data = self.database.animals.find(searchData, {"_id": False})
else:
data = self.database.animals.find({}, {"_id": False})
return data
#Create method to implement U in CRUD.
def update(self, searchData, updateData):
if searchData is not None:
result = self.database.animals.update_many(searchData, {"$set": updateData})
else:
return "{}"
return result.raw_result
#Create method to implement D in CRUD.
def delete(self, deleteData):
if deleteData is not None:
result = self.database.animals.delete_many(deleteData)
else:
return "{}"
return result.raw_result
.ipynb file code:
from jupyter_plotly_dash import JupyterDash
import dash
import dash_leaflet as dl
from dash import dcc
from dash import html
import pandas as pd
import plotly.express as px
from dash import dash_table as dt
from dash.dependencies import Input, Output, State
import os
import numpy as np
from pymongo import MongoClient
from bson.json_util import dumps
import base64
#### FIX ME #####
# change animal_shelter and AnimalShelter to match your CRUD Python module file name and class name
from AnimalShelterTest import AnimalShelter
###########################
# Data Manipulation / Model
###########################
# FIX ME change for your username and password and CRUD Python module name
username = "aacuser"
password = "French"
shelter = AnimalShelter(username, password)
# class read method must support return of cursor object
df = pd.DataFrame.from_records(shelter.read({}))
#########################
# Dashboard Layout / View
#########################
app = JupyterDash('Project Two')
#FIX ME Add in Grazioso Salvare’s logo
image_filename = 'Grazioso_Salvare_Logo.png' # replace with your own image
encoded_image = base64.b64encode(open(image_filename, 'rb').read())
#FIX ME Place the HTML image tag in the line below into the app.layout code according to your design
#FIX ME Also remember to include a unique identifier such as your name or date
#html.Img(src='data:image/png;base64,{}'.format(encoded_image.decode()))
app.layout = html.Div([
html.Div(id='hidden-div', style={'display':'none'}),
html.Center(html.Img(src='data:image/png;base64,{}'.format(encoded_image.decode()))),
html.Center(html.B(html.H1('Dashboard'))),
html.Hr(),
html.Div(
#FIXME Add in code for the interactive filtering options. For example, Radio buttons, drop down, checkboxes, etc.
dcc.RadioItems(
id='filter-type',
options=[
{'label': 'Water Rescue', 'value': 'WR'},
{'label': 'Mountain or Wilderness Rescue', 'value': 'MWR'},
{'label': 'Disaster or Individual Tracking', 'value': 'DIT'},
{'label': 'Reset','value':'RESET'}
],
value='RESET',
labelStyle={'display':'inline-block'}
)
),
html.Hr(),
dt.DataTable(
id='datatable-id',
columns=[
{"name": i, "id": i, "deletable": False, "selectable": True} for i in df.columns
],
data=df.to_dict('records'),
#FIXME: Set up the features for your interactive data table to make it user-friendly for your client
#If you completed the Module Six Assignment, you can copy in the code you created here
editable=False,
filter_action="native",
sort_action="native",
sort_mode="multi",
column_selectable=False,
row_selectable="single",
row_deletable=False,
selected_columns=[],
selected_rows=[],
page_action="native",
page_current= 0,
page_size= 10,
),
html.Br(),
html.Hr(),
#This sets up the dashboard so that your chart and your geolocation chart are side-by-side
html.Div(className='row',
style={'display' : 'flex'},
children=[
html.Div(
id='graph-id',
className='col s12 m6',
),
html.Div(
id='map-id',
className='col s12 m6',
)
])
])
#############################################
# Interaction Between Components / Controller
#############################################
@app.callback([Output('datatable-id','data'),
Output('datatable-id','columns')],
[Input('filter-type', 'value')])
def update_dashboard(filter_type):
### FIX ME Add code to filter interactive data table with MongoDB queries
if filter_type == 'WR':
df = pd.DataFrame(list(shelter.read({'$and': [{'sex_upon_outcome': 'Intact Female'},
{'$or': [
{'breed': 'Labrador Retriever Mix'},
{'breed': 'Chesa Bay Retr Mix'},
{'breed': 'Newfoundland Mix'},
{'breed': 'Newfoundland/Labrador Retriever'},
{'breed': 'Newfoundland/Australian Cattle Dog'},
{'breed': 'Newfoundland/Great Pyrenees'}]
},
{'$and': [{'age_upon_outcome_in_weeks': {'$gte': 26}},
{'age_upon_outcome_in_weeks': {'$lte': 156}}]
}]
})))
elif filter_type == 'MWR':
#Grazioso breeds and ages
df = pd.DataFrame(list(shelter.read({'$and': [{'sex_upon_outcome': 'Intact Male'},
{'$or': [
{'breed': 'German Shepherd'},
{'breed': 'Alaskan Malamute'},
{'breed': 'Old English Sheepdog'},
{'breed': 'Rottweiler'},
{'breed': 'Siberian Husky'}]
},
{'$and': [{'age_upon_outcome_in_weeks': {'$gte': 26}},
{'age_upon_outcome_in_weeks': {'$lte': 156}}]
}]
})))
#adjusts the read request for the desired dog type and status
elif filter_type == 'DRIT':
#breeds and ages
df = pd.DataFrame(list(shelter.read({'$and': [{'sex_upon_outcome': 'Intact Male'},
{'$or': [
{'breed': 'Doberman Pinscher'},
{'breed': 'German Shepherd'},
{'breed': 'Golden Retriever'},
{'breed': 'Bloodhound'},
{'breed': 'Rottweiler'}]
},
{'$and': [{'age_upon_outcome_in_weeks': {'$gte': 20}},
{'age_upon_outcome_in_weeks': {'$lte': 300}}]
}]
})))
#resets the search no filter
elif filter_type == 'RESET':
df = pd.DataFrame.from_records(shelter.read({}))
columns=[{"name": i, "id": i, "deletable": False, "selectable": True} for i in df.columns]
data=df.to_dict('records')
return (data,columns)
@app.callback(
Output('datatable-id', 'style_data_conditional'),
[Input('datatable-id', 'selected_columns')]
)
def update_styles(selected_columns):
return [{
'if': { 'column_id': i },
'background_color': '#D2F3FF'
} for i in selected_columns]
@app.callback(
Output('graph-id', "children"),
[Input('datatable-id', "derived_viewport_data")])
def update_graphs(viewData):
###FIX ME ####
dff = pd.DataFrame.from_dict(viewData)
names = dff['breed'].value_counts().keys().tolist()
values = dff['breed'].value_counts().tolist()
# add code for chart of your choice (e.g. pie chart) #
return [
dcc.Graph(
figure = px.pie(
data_frame = dff,
values = values,
names = names,
color_discrete_sequence=px.colors.sequential.RdBu,
width = 800,
height = 500
)
)
]
@app.callback(
Output('map-id', "children"),
[Input('datatable-id', "derived_viewport_data"),
Input('datatable-id', 'selected_rows'),
Input('datatable-id', 'selected_columns')])
def update_map(viewData, selected_rows, selected_columns):
#FIXME: Add in the code for your geolocation chart
#If you completed the Module Six Assignment, you can copy in the code you created here.
dff = pd.DataFrame.from_dict(viewData)
if selected_rows == []:
selected_rows = [0]
# Austin TX is at [30.75, -97.48]
if len(selected_rows) == 1:
return [
dl.Map(style={'width':'1000px', 'height': '500px'}, center=[30.75,-97.48], zoom=10, children=[
dl.TileLayer(id="base-layer-id"),
#marker with tool tip and popup
dl.Marker(position=[(dff.iloc[selected_rows[0],13]), (dff.iloc[selected_rows[0],14])], children=[
dl.Tooltip(dff.iloc[selected_rows[0],4]),
dl.Popup([
html.H4("Animal Name"),
html.P(dff.iloc[selected_rows[0],9]),
html.H4("Sex"),
html.P(dff.iloc[selected_rows[0],12]),
html.H4("Breed"),
html.P(dff.iloc[selected_rows[0],4]),
html.H4("Age"),
html.P(dff.iloc[selected_rows[0],15])
])
])
])
]
app
Here is the full error code I am getting:
TypeError Traceback (most recent call last)
Cell In [1], line 32
30 username = "aacuser"
31 password = "French"
---> 32 shelter = AnimalShelter(username, password)
35 # class read method must support return of cursor object
36 df = pd.DataFrame.from_records(shelter.read({}))
File ~\Sample Python Code\7-2 Project Two files\AnimalShelterTest.py:13, in AnimalShelter.__init__(self, username, password)
11 connection = pymongo.MongoClient("localhost:27017")
12 db = connection['AAC']
---> 13 connection.api.authenticate(username, password)
File C:\Python\Python311\Lib\site-packages\pymongo\collection.py:3200, in Collection.__call__(self, *args, **kwargs)
3198 """This is only here so that some API misusages are easier to debug."""
3199 if "." not in self.__name:
-> 3200 raise TypeError(
3201 "'Collection' object is not callable. If you "
3202 "meant to call the '%s' method on a 'Database' "
3203 "object it is failing because no such method "
3204 "exists." % self.__name
3205 )
3206 raise TypeError(
3207 "'Collection' object is not callable. If you meant to "
3208 "call the '%s' method on a 'Collection' object it is "
3209 "failing because no such method exists." % self.__name.split(".")[-1]
3210 )
TypeError: 'Collection' object is not callable. If you meant to call the 'authenticate' method on a 'Database' object it is failing because no such method exists.
I originally had the AnimalShelterTest.py code to pull the database using
self.client = MongoClient('mongodb://localhost:27017/' % (username, password))
self.database = self.client['AAC']
and this gave me the error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In [1], line 32
30 username = "aacuser"
31 password = "French"
---> 32 shelter = AnimalShelter(username, password)
35 # class read method must support return of cursor object
36 df = pd.DataFrame.from_records(shelter.read({}))
File ~\Sample Python Code\7-2 Project Two files\AnimalShelter.py:11, in AnimalShelter.__init__(self, username, password)
9 def __init__(self, username, password):
10 #Initializing the MongoClient. This helps to access the MongoDB databases and collections.
---> 11 self.client = MongoClient('mongodb://localhost:27017/' % (username, password))
12 #where xxxx is your unique port number
13 self.database = self.client['AAC']
TypeError: not all arguments converted during string formatting
1 answers
solution1
0 ACCPTED 2022-11-15 22:33:25
self.client = MongoClient('mongodb://localhost:27017/'format.(username, password))
this alteration fixed my problem
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