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read data come from firebase using pandas in python

I have read data from firebase in python. and i want to read it using pandas.
The code:
import pandas as pd
from firebase_admin import credentials
from firebase_admin import firestore
import firebase_admin
# get list data
cred = "card.json"
login = credentials.Certificate(cred)
#initialize firebase
# firebase_admin.initialize_app(login)
#reading from the database
db = firestore.client()
lists = db.collection("Lists").stream()
It work just fine. but when I try to use pandas. I'm not sure how to do it. I tried this.
listing = pd.DataFrame(lists)
print(listing)
it printed this:
Empty DataFrame
Columns: []
Index: []
am I missing something? am I doing something wrong? please help.

I created empty data frame. just added the column name.
then go through the data coming from firebase then add it to the data frame that I created.
like this:
data = {'ListID': [], 'Title': [], 'Description': [], 'Cover': [], 'Access': [], 'uid': []}
df = pd.DataFrame(data)
for list in lists:
l = list.to_dict()
df2 = pd.DataFrame({'ListID': [l.get("ListID")],
'Title': [l.get("Title")],
'Description': [l.get("Description")],
'Cover': [l.get("Cover")],
'Access': [l.get("Access")],
'uid': [l.get("uid")]})
df = pd.concat([df, df2], ignore_index = True, axis = 0)

Creating multiple dataframe using loop or function

I'm trying to extract the hash rate for 3 cryptocurrencies and I have attached the code for the same below. Now, I want to pass three urls and in return I need three different different dictionaries which should have the values. I'm stuck and I don't understand how should I go about it. I have tried using loops but it is not working out for me.
url = {'Bitcoin' : 'https://bitinfocharts.com/comparison/bitcoin-hashrate.html#3y',
'Ethereum': 'https://bitinfocharts.com/comparison/ethereum-hashrate.html#3y',
'Litecoin': 'https://bitinfocharts.com/comparison/litecoin-hashrate.html'}
for ele in url:
#### requesting the page and extracting the script which has date and values
session = requests.Session()
page = session.get(ele[i])
soup = BeautifulSoup(page.content, 'html.parser')
values = str(soup.find_all('script')[4])
values = values.split('d = new Dygraph(document.getElementById("container"),')[1]
#create an empty dict to append date and hashrates
dict([("crypto_1 %s" % i,[]) for i in range(len(url))])
#run a loop over all the dates and adding to dictionary
for i in range(values.count('new Date')):
date = values.split('new Date("')[i+1].split('"')[0]
value = values.split('"),')[i+1].split(']')[0]
dict([("crypto_1 %s" % i)[date] = value

You can use next example how to get data from all 3 URLs and create a dataframe/dictionary from it:
import re
import requests
import pandas as pd
url = {
"Bitcoin": "https://bitinfocharts.com/comparison/bitcoin-hashrate.html#3y",
"Ethereum": "https://bitinfocharts.com/comparison/ethereum-hashrate.html#3y",
"Litecoin": "https://bitinfocharts.com/comparison/litecoin-hashrate.html",
}
data = []
for name, u in url.items():
html_doc = requests.get(u).text
for date, hash_rate in re.findall(
r'\[new Date\("(.*?)"\),(.*?)\]', html_doc
):
data.append(
{
"Name": name,
"Date": date,
"Hash Rate": float("nan")
if hash_rate == "null"
else float(hash_rate),
}
)
df = pd.DataFrame(data)
df["Date"] = pd.to_datetime(df["Date"])
# here save df to CSV
# this will create a dictionary, where the keys are crypto names and values
# are dicts with keys Date/HashRate:
out = {}
for name, g in df.groupby("Name"):
out[name] = g[["Date", "Hash Rate"]].to_dict(orient="list")
print(out)
Prints:
{
"Bitcoin": {
"Date": [
Timestamp("2009-01-03 00:00:00"),
Timestamp("2009-01-04 00:00:00"),
Timestamp("2009-01-05 00:00:00"),
...

Json file not formatted correctly when writing json differences with pandas and numpy

I am trying to compare two json and then write another json with columns names and with differences as yes or no. I am using pandas and numpy
The below is sample files i am including actually, these json are dynamic, that mean we dont know how many key will be there upfront
Input files:
fut.json
[
{
"AlarmName": "test",
"StateValue": "OK"
}
]
Curr.json:
[
{
"AlarmName": "test",
"StateValue": "OK"
}
]
Below code I have tried:
import pandas as pd
import numpy as np
with open(r"c:\csv\fut.json", 'r+') as f:
data_b = json.load(f)
with open(r"c:\csv\curr.json", 'r+') as f:
data_a = json.load(f)
df_a = pd.json_normalize(data_a)
df_b = pd.json_normalize(data_b)
_, df_a = df_b.align(df_a, fill_value=np.NaN)
_, df_b = df_a.align(df_b, fill_value=np.NaN)
with open(r"c:\csv\report.json", 'w') as _file:
for col in df_a.columns:
df_temp = pd.DataFrame()
df_temp[col + '_curr'], df_temp[col + '_fut'], df_temp[col + '_diff'] = df_a[col], df_b[col], np.where((df_a[col] == df_b[col]), 'No', 'Yes')
#[df_temp.rename(columns={c:'Missing'}, inplace=True) for c in df_temp.columns if df_temp[c].isnull().all()]
df_temp.fillna('Missing', inplace=True)
with pd.option_context('display.max_colwidth', -1):
_file.write(df_temp.to_json(orient='records'))
Expected output:
[
{
"AlarmName_curr": "test",
"AlarmName_fut": "test",
"AlarmName_diff": "No"
},
{
"StateValue_curr": "OK",
"StateValue_fut": "OK",
"StateValue_diff": "No"
}
]
Coming output: Not able to parse it in json validator, below is the problem, those [] should be replaed by ',' to get right json dont know why its printing like that
[{"AlarmName_curr":"test","AlarmName_fut":"test","AlarmName_diff":"No"}][{"StateValue_curr":"OK","StateValue_fut":"OK","StateValue_diff":"No"}]
Edit1:
Tried below as well
_file.write(df_temp.to_json(orient='records',lines=True))
now i get json which is again not parsable, ',' is missing and unless i add , between two dic and [ ] at beginning and end manually , its not parsing..
[{"AlarmName_curr":"test","AlarmName_fut":"test","AlarmName_diff":"No"}{"StateValue_curr":"OK","StateValue_fut":"OK","StateValue_diff":"No"}]

Honestly pandas is overkill for this... however
load dataframes as you did
concat them as columns. rename columns
do calcs and map boolean to desired Yes/No
to_json() returns a string so json.loads() to get it back into a list/dict. Filter columns to get to your required format
import json
data_b = [
{
"AlarmName": "test",
"StateValue": "OK"
}
]
data_a = [
{
"AlarmName": "test",
"StateValue": "OK"
}
]
df_a = pd.json_normalize(data_a)
df_b = pd.json_normalize(data_b)
df = pd.concat([df_a, df_b], axis=1)
df.columns = [c+"_curr" for c in df_a.columns] + [c+"_fut" for c in df_a.columns]
df["AlarmName_diff"] = df["AlarmName_curr"] == df["AlarmName_fut"]
df["StateValue_diff"] = df["StateValue_curr"] == df["StateValue_fut"]
df = df.replace({True:"Yes", False:"No"})
js = json.loads(df.loc[:,(c for c in df.columns if c.startswith("Alarm"))].to_json(orient="records"))
js += json.loads(df.loc[:,(c for c in df.columns if c.startswith("State"))].to_json(orient="records"))
js
output
[{'AlarmName_curr': 'test', 'AlarmName_fut': 'test', 'AlarmName_diff': 'Yes'},
{'StateValue_curr': 'OK', 'StateValue_fut': 'OK', 'StateValue_diff': 'Yes'}]

How to use pandas DF as params in HTTP request

I have a list of places from an excel file which I would enrich with the geonames Ids. Starting from the excel file I made a pandas Data Frame then I would use the values from the DF as params in my request.
Here the script I made
import pandas as pd
import requests
import json
require_cols = [1]
required_df = pd.read_excel('grp.xlsx', usecols = require_cols)
print(required_df)
url = 'http://api.geonames.org/searchJSON?'
params = { 'username': "XXXXXXXX",
'name_equals': (required_df),
'maxRows': "1"}
e = requests.get(url, params=params)
pretty_json = json.loads(e.content)
print (json.dumps(pretty_json, indent=2))
The problem is related to the defintion of this parameter:
'name_equals': (required_df)
I would use the Places (around 15k) from the DF as param and recoursively retrieve the related geonames ID and write the output in a separate excel file.
The simple request works:
import requests
import json
url = 'http://api.geonames.org/searchJSON?'
params = { 'username': "XXXXXXX",
'name_equals': "Aire",
'maxRows': "1"}
e = requests.get(url, params=params)
pretty_json = json.loads(e.content)
print (json.dumps(pretty_json, indent=2))
#print(e.content)
As well as the definition of Pandas data frame:
# import pandas lib as pd
import pandas as pd
require_cols = [0,1]
# only read specific columns from an excel file
required_df = pd.read_excel('grp.xlsx', usecols = require_cols)
print(required_df)
I also tried via SPARQL without results so I decided to go via Python.
Thanks for your time.

You can use for-loop
import pandas as pd
df = pd.DataFrame({'Places': ['London', 'Paris', 'Berlin']})
for item in df['Places']:
print('requests for:', item)
# ... rest of code ...
or df.apply()
import pandas as pd
def run(item):
print('requests for:', item)
# ... rest of code ...
return 'result for ' + item
df = pd.DataFrame({'Places': ['London', 'Paris', 'Berlin']})
df['Results'] = df['Places'].apply(run)

Thanks #furas for your reply.
I solved like this:
import pandas as pd
import requests
import json
url = 'http://api.geonames.org/searchJSON?'
df = pd.read_excel('Book.xlsx', sheet_name='Sheet1', usecols="B")
for item in df.place_name:
df.place_name.head()
params ={ 'username': "XXXXXX",
'name_equals': item,
'maxRows': "1"}
e = requests.get(url, params=params)
pretty_json = json.loads(e.content)
for item in pretty_json["geonames"]:
print (json.dumps(item["geonameId"], indent=2))
with open('data.json', 'w', encoding='utf-8') as f:
json.dump(item["geonameId"], f, ensure_ascii=False, indent=4)
#print(e.content)
The only problem now is related to the json output: By print I'm having the complete IDs list however, when I'm going to write the output to a file I'm getting just the last ID from the list.

What is the data format returned by the AdWords API TargetingIdeaPage service?

When I query the AdWords API to get search volume data and trends through their TargetingIdeaSelector using the Python client library the returned data looks like this:
(TargetingIdeaPage){
totalNumEntries = 1
entries[] =
(TargetingIdea){
data[] =
(Type_AttributeMapEntry){
key = "KEYWORD_TEXT"
value =
(StringAttribute){
Attribute.Type = "StringAttribute"
value = "keyword phrase"
}
},
(Type_AttributeMapEntry){
key = "TARGETED_MONTHLY_SEARCHES"
value =
(MonthlySearchVolumeAttribute){
Attribute.Type = "MonthlySearchVolumeAttribute"
value[] =
(MonthlySearchVolume){
year = 2016
month = 2
count = 2900
},
...
(MonthlySearchVolume){
year = 2015
month = 3
count = 2900
},
}
},
},
}
This isn't JSON and appears to just be a messy Python list. What's the easiest way to flatten the monthly data into a Pandas dataframe with a structure like this?
Keyword | Year | Month | Count
keyword phrase 2016 2 10

The output is a sudsobject. I found that this code does the trick:
import suds.sudsobject as sudsobject
import pandas as pd
a = [sudsobject.asdict(x) for x in output]
df = pd.DataFrame(a)
Addendum: This was once correct but new versions of the API (I tested
201802) now return a zeep.objects. However, zeep.helpers.serialize_object should do the same trick.
link

Here's the complete code that I used to query the TargetingIdeaSelector, with requestType STATS, and the method I used to parse the data to a useable dataframe; note the section starting "Parse results to pandas dataframe" as this takes the output given in the question above and converts it to a dataframe. Probably not the fastest or best, but it works! Tested with Python 2.7.
"""This code pulls trends for a set of keywords, and parses into a dataframe.
The LoadFromStorage method is pulling credentials and properties from a
"googleads.yaml" file. By default, it looks for this file in your home
directory. For more information, see the "Caching authentication information"
section of our README.
"""
from googleads import adwords
import pandas as pd
adwords_client = adwords.AdWordsClient.LoadFromStorage()
PAGE_SIZE = 10
# Initialize appropriate service.
targeting_idea_service = adwords_client.GetService(
'TargetingIdeaService', version='v201601')
# Construct selector object and retrieve related keywords.
offset = 0
stats_selector = {
'searchParameters': [
{
'xsi_type': 'RelatedToQuerySearchParameter',
'queries': ['donald trump', 'bernie sanders']
},
{
# Language setting (optional).
# The ID can be found in the documentation:
# https://developers.google.com/adwords/api/docs/appendix/languagecodes
'xsi_type': 'LanguageSearchParameter',
'languages': [{'id': '1000'}],
},
{
# Location setting
'xsi_type': 'LocationSearchParameter',
'locations': [{'id': '1027363'}] # Burlington,Vermont
}
],
'ideaType': 'KEYWORD',
'requestType': 'STATS',
'requestedAttributeTypes': ['KEYWORD_TEXT', 'TARGETED_MONTHLY_SEARCHES'],
'paging': {
'startIndex': str(offset),
'numberResults': str(PAGE_SIZE)
}
}
stats_page = targeting_idea_service.get(stats_selector)
##########################################################################
# Parse results to pandas dataframe
stats_pd = pd.DataFrame()
if 'entries' in stats_page:
for stats_result in stats_page['entries']:
stats_attributes = {}
for stats_attribute in stats_result['data']:
#print (stats_attribute)
if stats_attribute['key'] == 'KEYWORD_TEXT':
kt = stats_attribute['value']['value']
else:
for i, val in enumerate(stats_attribute['value'][1]):
data = {'keyword': kt,
'year': val['year'],
'month': val['month'],
'count': val['count']}
data = pd.DataFrame(data, index = [i])
stats_pd = stats_pd.append(data, ignore_index=True)
print(stats_pd)