I'm trying to stream Live Quotes using the IexFinance API, keep in mind this is my first coding attempt. I've managed to be get the stock quote prices through python but I'm unsure how I would get that data then onto Excel.
From my understanding I would need to get this data into a csv file in order to export that into excel. I've tried adding the code df.to_csv('stock.csv') but I get the error 'StockReader' object has no attribute 'to_csv'
import pandas as pd
from iexfinance.stocks import stock
batch=Stock(['amd', 'tsla'], output_format='pandas')
batch.get_price
df.to_csv('stock.csv')
General pointer: Looks like you need to read into df variable first.
This line:
Stock(['amd', 'tsla'], output_format='pandas')
According to the guidance returns a dataframe
So:
df = Stock(['amd', 'tsla'], output_format='pandas')
Or, as you have now discovered:
df = batch.get_price
Related
I have an excel file that contains the names of 60 datasets.
I'm trying to write a piece of code that "enters" the Excel file, accesses a specific dataset (whose name is in the Excel file), gathers and analyses some data and finally, creates a new column in the Excel file and inserts the information gathered beforehand.
I can do most of it, except for the part of adding a new column and entering the data.
I was trying to do something like this:
path_data = **the path to the excel file**
recap = pd.read_excel(os.path.join(path_data,'My_Excel.xlsx')) # where I access the Excel file
recap['New information Column'] = Some Value
Is this a correct way of doing this? And if so, can someone suggest a better way (that works ehehe)
Thank you a lot!
You can import the excel file into python using pandas.
import pandas as pd
df = pd.read_excel (r'Path\Filename.xlsx')
print (df)
If you have many sheets, then you could do this:
import pandas as pd
df = pd.read_excel (r'Path\Filename.xlsx', sheet_name='sheetname')
print (df)
To add a new column you could do the following:
df['name of the new column'] = 'things to add'
Then when you're ready, you can export it as xlsx:
import openpyxl
# to excel
df.to_excel(r'Path\filename.xlsx')
I have a CSV file, diseases_matrix_KNN.csv which has excel table.
Now, I would like to store all the numbers from the row like:
Hypothermia = [0,-1,0,0,0,0,0,0,0,0,0,0,0,0]
For some reason, I am unable to find a solution to this. Even though I have looked. Please let me know if I can read this type of data in the chosen form, using Python please.
most common way to work with excel is use Pandas.
Here is example:
import pandas as pd
df = pd.read_excel(filename)
print (df.iloc['Hypothermia']). # gives you such result
I am trying to understand how JSON data which is not parsed/extracted correctly can be converted into a (Pandas) DataFrame.
I am using python (3.7.1) and have tried the usual way of reading the JSON data. Actually, the code works if I use transpose or axis=1 syntax. But using that completely ignores a large number of values or variables in the data and I am 100% sure that the maybe the code is working but is not giving the desired results.
import pandas as pd
import numpy as np
import csv
import json
sourcefile = open(r"C:\Users\jadil\Downloads\chicago-red-light-and-speed-camera-data\socrata_metadata_red-light-camera-violations.json")
json_data = json.load(sourcefile)
#print(json_data)
type(json_data)
dict
## this code works but is not loading/reading complete data
df = pd.DataFrame.from_dict(json_data, orient="index")
df.head(15)
#This is what I am getting for the first 15 rows
df.head(15)
0
createdAt 1407456580
description This dataset reflects the daily volume of viol...
rights [read]
flags [default, restorable, restorePossibleForType]
id spqx-js37
oid 24980316
owner {'type': 'interactive', 'profileImageUrlLarge'...
newBackend False
totalTimesRated 0
attributionLink http://www.cityofchicago.org
hideFromCatalog False
columns [{'description': 'Intersection of the location...
displayType table
indexUpdatedAt 1553164745
rowsUpdatedBy n9j5-zh
As you have seen, Pandas will attempt to create a data frame out of JSON data even if it is not parsed or extracted correctly. If your goal is to understand exactly what Pandas does when presented with a messy JSON file, you can look inside the code for pd.DataFrame.from_dict() to learn more. If your goal is to get the JSON data to convert correctly to a Pandas data frame, you will need to provide more information abut the JSON data, ideally by providing a sample of the data as text in your question. If your data is sufficiently complicated, you might try the json_normalize() function as described here.
I am trying to restructure the way my precipitations' data is being organized in an excel file. To do this, I've written the following code:
import pandas as pd
df = pd.read_excel('El Jem_Souassi.xlsx', sheetname=None, header=None)
data=df["El Jem"]
T=[]
for column in range(1,56):
liste=data[column].tolist()
for row in range(1,len(liste)):
liste[row]=str(liste[row])
if liste[row]!='nan':
T.append(liste[row])
result=pd.DataFrame(T)
result
This code works fine and through Jupyter I can see that the result is good
screenshot
However, I am facing a problem when attempting to save this dataframe to a csv file.
result.to_csv("output.csv")
The resulting file contains the vertical index column and it seems I am unable to call for a specific cell.
(Hopefully, someone can help me with this problem)
Many thanks !!
It's all in the docs.
You are interested in skipping the index column, so do:
result.to_csv("output.csv", index=False)
If you also want to skip the header add:
result.to_csv("output.csv", index=False, header=False)
I don't know how your input data looks like (it is a good idea to make it available in your question). But note that currently you can obtain the same results just by doing:
import pandas as pd
df = pd.DataFrame([0]*16)
df.to_csv('results.csv', index=False, header=False)
In order to save stock prices from yahoo into Python 3.5, I use the pandas module :
from pandas_datareader import data as dreader
symbols = ['AAPL','MRK']
pnls = {i:dreader.DataReader(i,'yahoo','2010-01-01','2016-09-01') for i in symbols}
It creates two "tables" (I don't know the name, sorry), one for each share (here 'AAPL' and 'MRK'). I want to save each table into a csv file but I don't know how. Anyone does?
Thanks,
Anthony
Just do this:
from pandas_datareader import data as dreader
symbols = ['AAPL','MRK']
for i in symbols:
dreader.DataReader(i,'yahoo','2010-01-01','2016-09-01').to_csv(i+'.csv')
It saves your data to two csv files.
It actually returns a pandas DataFrame. You can easily put a pandas DataFrame to csv file using the to_csv method.