nsepy | Python Library to get publicly available data | Data Visualization library
kandi X-RAY | nsepy Summary
kandi X-RAY | nsepy Summary
I'm putting about 1 Hr per week on NSEpy as hobby and I will continue to do so. but as this effort is just not enough, NSEpy at the moment is short of good documentation. There are lot of features in NSEpy still not documented :( , so till we complete the documentation, I'll need support from the community. Please write about your projects in blogs, quora answers and other forums, so that people find working examples to get started.
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Top functions reviewed by kandi - BETA
- Get historical data
- Validate parameters
- Returns a pandas DataFrame of historical trades
- Convert url to pandas DataFrame
- Get a pandas dataframe from a query
- Get symbol count
- Create pandas dataframe
- Print pehistory
- Returns a pandas DataFrame containing the data for a given symbol
- Wrapper for get_index_pe_history
- Get rbi reference history
- Returns a pandas dataframe from rbi_ref_history
- Get price list for a given date
- Unzip a string
- Return a default format
- Returns a table containing the option chain
- Returns an option chain
- Run the task
nsepy Key Features
nsepy Examples and Code Snippets
Lines of Code : 32License : Strong Copyleft (CC BY-SA 4.0)
Copyimport pandas as pd
from nsepy import get_history
from datetime import date
import concurrent.futures
history = {
"SBIN": {"start": date(2021, 1, 1), "end": date(2022, 1, 31)},
"SAIL": {"start": date(2021, 1, 1), "end": date(2022,import pandas as pd
import numpy as np
from datetime import date
from nsepy import get_history
from keras.models import Sequential
from keras.layers import LSTM, Dense
from sklearn.preprocessing import MinMaxScaler
pd.options.mode.chained_df[df['Date'].str.endswith('12-31')]
df[df.assign(Date=df['Date'].astype(str))['Date'].str.endswith('12-31')]
df['Date1'] = pd.to_datetime(df['Date'])
df['BYearEnd'] = pd.to_datetime(dfdf['Date'] = pd.to_datetime(df['Date'])
df.loc[(df.Date.dt.month == 12) & (df.Date.dt.day == 31)]
Date Price
1 2010-12-31 25
3 2013-12-31 28
5 2016-12-31 28
8 2020-import numpy as np
import pandas as pd
from nsepy import get_history
from datetime import date
bankniftydata = get_history(symbol='BANKNIFTY',start=date(2016,5,30),end=date(2016,6,10),index=True)
df = bankniftydata.reset_index()
df['Datnew_df = df.pivot_table(index='Date',columns='Symbol',value='trend')
lst = list(res.values())
df = pd.concat(lst)
for key, df in res.items():
# create a column called "key name"
df['key_name'] = key
lst = list(res.values())
df = pd.concat(lst)
res={}
for stock_name in stock:
data = get_history(symbol=stock_name, start=start, end=end)
res[stock_name]=data
sbin.index = pd.to_datetime(sbin.index)
sbin = sbin.resample('W').agg({'High': 'max', 'Low': 'min', 'Close': 'last'})
print(sbin)
High Low Close
Date
2020-10-18 205.95 191.60 195.95
202from zipfile import BadZipFile
Community Discussions
Trending Discussions on nsepy
QUESTION
I have a pandas dataframe where I want to loop over its rows, run and save output, if any error then ingore it and move to next row.
...ANSWER
Answered 2022-Feb-13 at 09:54As your process is IO-Bounded, you can use Threading to increase the speed.
You can try this:
QUESTION
I have trained my stock price prediction model by splitting the dataset into train & test. I have also tested the predictions by comparing the valid data with the predicted data, and the model works fine. But I want to predict actual future values.
What do I need to change in my code below?
How can I make predictions up to a specific date in the actual future?
Code (in a Jupyter Notebook):
(To run the code, please try it in a similar csv file you have, or install nsepy python library using command pip install nsepy)
ANSWER
Answered 2021-Dec-22 at 10:12Below is an example of how you could implement this approach for your model:
QUESTION
I am using the below code in pycharm:-
...ANSWER
Answered 2021-Oct-25 at 20:26Can confirm that copying and pasting your code returns data in the console as you can see below. I suspect you are running another script/tab when you click run in PyCharm.
QUESTION
I have dataset containing EOD OHLC for Banknifty Index of India's National Stock Exchange.
Goal : I want to split OHLC data on a weekly basis.
You can get the relevant data using the below code:
...ANSWER
Answered 2021-Jan-01 at 13:48Although I think that the output 2 is the answer, the following code gives the result in the form of DataFrames:
QUESTION
I have a dataframe which looks like this:
I wanted to make a dataframe which looks like this:
For this I have referred the post at pandas convert some columns into rows.
By using the merge function I get a dataframe as shown below:
How do I get my dataframe in the format required?
The complete code is as shown:
...ANSWER
Answered 2020-Dec-22 at 14:34IIUC, this can be solved with pivot_table():
Given the original dataframe you show in the first image:
QUESTION
I am trying to make an application which tries to find the trending stocks in the last 6 months. The application checks for two conditions namely:
- Is (previous close) < (close) If yes then True is returned
- Else false is returned.
After determining the above two conditions the programme checks how many true values are there in succession if there are multiple true values add 1 to the Trending Counter and append it as a column. For false values counter has to be set as 0.
The code for the same is:
...ANSWER
Answered 2020-Dec-16 at 17:36Generally speaking, you can create numbered groups of False entries - the True entries will be NaN, then forward fill to fill in True entries that occur after False entries. Then just fill your early True values with a sentinel value df["trend"].fillna(value=1, inplace=True) and use where to fill everything except your sentinels with 0.
This approach is easily adapted to a number of different problems relating to trends that have a boolean component.
Here's working code - you can split the lines and print out the data at each step if you want to be sure you know how this works:
QUESTION
I have a dictionary which contains a large no of data frames within it. I want to extract all the data frames from it and stores those values into a single dataframe. The dictionary is as shown:
One of the many dataframes which are stored in the dictionary is as shown:
I have tried using the 'pd.DataFrame.from_dict' method as shown:
...ANSWER
Answered 2020-Dec-12 at 15:23Since the values in the dictionary are already dataframes, I don't think you want to try to convert the dictionary into a DataFrame directly. Rather, iterate over the dictionary and add all the values to a new DataFrame.
Do you care about the keys in the dictionary?
If not one possible solution would be to make a list just using the values in the dictionary, then combine into a DataFrame with pd.concat.
QUESTION
I am writing a code for downloading the historical data for multiple stocks. The code is as given:
...ANSWER
Answered 2020-Dec-10 at 12:30You would better create a dictionary with stocks as keys. See below:
QUESTION
I am using the nsepy module to get the data for 2 week for SBIN share.
Code :
...ANSWER
Answered 2020-Oct-25 at 14:42You can use resample for this. It will split it into 2 weeks, week of 10/18 and 10/25:
QUESTION
I am trying to extract the stock data for the past month and ran into this error when my code tries to extract stock data on days where there was no trading.
The code stops execution on datetime.date(2020, 7, 26) which was a Sunday and so there was no stock trade.
My code :
...ANSWER
Answered 2020-Jul-31 at 07:49You need to import the error you're trying to catch:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install nsepy
You can use nsepy like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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