I'm fairly new to python and I promise I looked around for a while before I came here but I'm trying to make a stock reader in which someone can just type in whichever stock they want and it shows the data for it. So far everything is going well but I am having trouble with the user input, here is my code:
from alpha_vantage.timeseries import TimeSeries
import matplotlib.pyplot as plt
pwd = input('Enter Ticker Symbol Here: ')
ts = TimeSeries(key='HQL2R9KNYW99K4BT', output_format='pandas')
data, meta_data = ts.get_intraday(symbol=**'TSLA'**, interval='1min', outputsize='full') *#But Instead of tesla I want it to be user input.*
data['4. close'].plot()
plt.title('Intraday Times Series for the MSFT stock (1 min)')
plt.show()
The error I am getting is:
Traceback (most recent call last):
File "C:/Users/abakh/PycharmProjects/stock1/Stock1.py", line 7, in <module>
data, meta_data = ts.get_intraday(symbol=' + pwd + ', interval='1min', outputsize='full')
File "C:\Users\abakh\PycharmProjects\stock1\venv\lib\site-packages\alpha_vantage\alphavantage.py", line 178, in _format_wrapper
data = call_response[data_key]
KeyError: 'Time Series (1min)'
Never mind guys, While waiting for a response I was messing around and actually found a way to do it! instead of making a seperate imput I added a input on the meta_date line it self : data, meta_data = ts.get_intraday(symbol=input('Put here: '), interval='1min', outputsize='full')
Related
I got the following code to elaborate on my problem. I'm using python 3.6 with pandas==0.25.3.
import pandas as pd
from enum import Enum, IntEnum
class BookType(Enum):
DRAMA = 5
ROMAN = 3
class AuthorType(IntEnum):
UNKNOWN = 0
GROUP = 1
MAN = 2
def print_num_type(df: pd.DataFrame, col_name: str, enum_type: Enum) -> int:
counts = df[col_name].value_counts()
val = counts[enum_type]
print('value counts:', counts)
print(f'Found "{val}" of type {enum_type}')
d = {'title': ['Charly Morry', 'James', 'Watson', 'Marry L.'], 'isbn': [21412412, 334764712, 12471021, 124141111], 'book_type': [BookType.DRAMA, BookType.ROMAN, BookType.ROMAN, BookType.ROMAN], 'author_type': [AuthorType.UNKNOWN, AuthorType.UNKNOWN, AuthorType.MAN, AuthorType.UNKNOWN]}
df = pd.DataFrame(data=d)
df.set_index(['title', 'isbn'], inplace=True)
df['book_type'] = df['book_type'].astype('category')
df['author_type'] = df['author_type'].astype('category')
print(df)
print(df.dtypes)
print_num_type(df, 'book_type', BookType.DRAMA)
print_num_type(df, 'author_type', AuthorType.UNKNOWN)
My pandas.DataFrame consists of two columns (book_type and author_type) of type categorical.
Furthermore, book_type is a class inheriting from type Enum and author_type from IntEnum. When calling print_num_type(df, 'book_type', BookType.DRAMA) everything works out as expected and the number of books of this type are printed, whereas print_num_type(df, 'author_type', AuthorType.UNKNOWN) raises the error:
Traceback (most recent call last):
File "C:\Users\User\AppData\Local\Programs\Python\Python36-32\lib\abc.py", line 182, in __instancecheck__
if subclass in cls._abc_cache:
File "C:\Users\User\AppData\Local\Programs\Python\Python36-32\lib\_weakrefset.py", line 72, in __contains__
wr = ref(item)
RecursionError: maximum recursion depth exceeded while calling a Python object
Exception ignored in: 'pandas._libs.lib.c_is_list_like'
Traceback (most recent call last):
File "C:\Users\User\AppData\Local\Programs\Python\Python36-32\lib\abc.py", line 182, in __instancecheck__
if subclass in cls._abc_cache:
File "C:\Users\User\AppData\Local\Programs\Python\Python36-32\lib\_weakrefset.py", line 72, in __contains__
wr = ref(item)
RecursionError: maximum recursion depth exceeded while calling a Python object
What am I doing wrong here?
Is there a workaround to get this error fixed? since I can't change the IntEnum type of AuthorType since it's provided from another library.
Thanks in advance!
See answer here
The main idea is that since x.value_counts() or counts in your function is itself a pandas Series, it's best to use .iat or .iloc when calling it, e.g, see iat docs
I think the easiest solution is to just use (x==0).sum(), or in your syntax:
val = (df[col_name]==enum_type).sum()
I put a minimal working example in the comments under your question so you can reproduce the problem/fix easily with the "x" notation.
What version of Pandas are you using? I realized after reproducing the error that upgrading Pandas (now on pandas-1.4.2) fixes the error, and the value_counts()[0] worked as expected.
run pip install --upgrade pandas
ran into a trouble downloading and reading csv files provided by US Department of Education National Center for Education Statistics. Below is code that should run for folks that might be interested in helping me troubleshoot.
import requests, zipfile, io
# First example shows that the code can work. Works fine on years 2005
# and earlier.
url = 'https://nces.ed.gov/ipeds/datacenter/data/HD2005_Data_Stata.zip'
r_zip_file_2005 = requests.get(url, stream=True)
z_zip_file_2005 = zipfile.ZipFile(io.BytesIO(r_zip_file_2005.content))
z_zip_file_2005.extractall('.')
csv_2005_df = pd.read_csv('hd2005_data_stata.csv')
# Second example shows that something changed in the CSV files after
# 2005 (or seems to have changed).
url = 'https://nces.ed.gov/ipeds/datacenter/data/HD2006_Data_Stata.zip'
r_zip_file_2006 = requests.get(url, stream=True)
z_zip_file_2006 = zipfile.ZipFile(io.BytesIO(r_zip_file_2006.content))
z_zip_file_2006.extractall('.')
csv_2006_df = pd.read_csv('hd2006_data_stata.csv')
For 2006 Python raises:
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x96 in position 18: invalid start byte
UnicodeDecodeError Traceback (most recent call last)
<ipython-input-26-b26a150e37ee> in <module>()
----> 1 csv_2006_df = pd.read_csv('hd2006_data_stata.csv')
Any tips on how to overcome this?
Only took 7 months... Figured my answer. Wasn't rocket science.
csv_2006_df = pd.read_csv('hd2006_data_stata.csv',
encoding='ISO-8859-1')
I have a file which is monthly data for one year (12 points). The data starts in December and ends in November. I'm hoping to create a 3-month running mean file which would be DJF, JFM, ..., SON (10 points)
I noticed there is a DataArray.rolling function which returns a rolling window option and I think would be useful for this. However, I haven't found any examples using the rolling function. I admit i'm not familiar with bottleneck, pandas.rolling_mean or the more recent pandas.rolling so my entry level is fairly low.
Here's some code to test:
import numpy as np
import pandas as pd
import xarray as xr
lat = np.linspace(-90, 90, num=181); lon = np.linspace(0, 359, num=360)
# Define monthly average time as day in middle of month
time = pd.date_range('15/12/1999', periods=12, freq=pd.DateOffset(months=1))
# Create data as 0:11 at each grid point
a = np.linspace(0,11,num=12)
# expand to 2D
a2d = np.repeat(tmp[:, np.newaxis], len(lat), axis=1)
# expand to 3D
a3d = np.repeat(a2d[:, :, np.newaxis], len(lon), axis=2)
# I'm sure there was a cleaner way to do that...
da = xr.DataArray(a3d, coords=[time, lat, lon], dims=['time','lat','lon'])
# Having a stab at the 3-month rolling mean
da.rolling(dim='time',window=3).mean()
# Error output:
Traceback (most recent call last):
File "<ipython-input-132-9d64cc09c263>", line 1, in <module>
da.rolling(dim='time',window=3).mean()
File "/Users/Ray/anaconda/lib/python3.6/site-packages/xarray/core/common.py", line 478, in rolling
center=center, **windows)
File "/Users/Ray/anaconda/lib/python3.6/site-packages/xarray/core/rolling.py", line 126, in __init__
center=center, **windows)
File "/Users/Ray/anaconda/lib/python3.6/site-packages/xarray/core/rolling.py", line 62, in __init__
raise ValueError('exactly one dim/window should be provided')
ValueError: exactly one dim/window should be provided
You are very close. The rolling method takes a key/value pair that maps as dim/window_size. This should work for you.
da.rolling(time=3).mean()
I am trying to replicate one example out of Wes McKinney's book on Pandas, the code is here (it assumes all names datafiles are under names folder)
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
years = range(1880, 2011)
pieces = []
columns = ['name', 'sex', 'births']
for year in years:
path = 'names/yob%d.txt' % year
frame = pd.read_csv(path, names=columns)
frame['year'] = year
pieces.append(frame)
names = pd.concat(pieces, ignore_index=True)
names
def get_tops(group):
return group.sort_index(by='births', ascending=False)[:1000]
grouped = names.groupby(['year','sex'])
grouped.apply(get_tops)
I am using Pandas 0.10 and Python 2.7. The error I am seeing is this:
Traceback (most recent call last):
File "names.py", line 21, in <module>
grouped.apply(get_tops)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.10.0-py2.7-linux-i686.egg/pandas/core/groupby.py", line 321, in apply
return self._python_apply_general(f)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.10.0-py2.7-linux-i686.egg/pandas/core/groupby.py", line 324, in _python_apply_general
keys, values, mutated = self.grouper.apply(f, self.obj, self.axis)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.10.0-py2.7-linux-i686.egg/pandas/core/groupby.py", line 585, in apply
values, mutated = splitter.fast_apply(f, group_keys)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.10.0-py2.7-linux-i686.egg/pandas/core/groupby.py", line 2127, in fast_apply
results, mutated = lib.apply_frame_axis0(sdata, f, names, starts, ends)
File "reduce.pyx", line 421, in pandas.lib.apply_frame_axis0 (pandas/lib.c:24934)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.10.0-py2.7-linux-i686.egg/pandas/core/frame.py", line 2028, in __setattr__
self[name] = value
File "/usr/local/lib/python2.7/dist-packages/pandas-0.10.0-py2.7-linux-i686.egg/pandas/core/frame.py", line 2043, in __setitem__
self._set_item(key, value)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.10.0-py2.7-linux-i686.egg/pandas/core/frame.py", line 2078, in _set_item
value = self._sanitize_column(key, value)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.10.0-py2.7-linux-i686.egg/pandas/core/frame.py", line 2112, in _sanitize_column
raise AssertionError('Length of values does not match '
AssertionError: Length of values does not match length of index
Any ideas?
I think this was a bug introduced in 0.10, namely issue #2605,
"AssertionError when using apply after GroupBy". It's since been fixed.
You can either wait for the 0.10.1 release, which shouldn't be too long from now, or you can upgrade to the development version (either via git or simply by downloading the zip of master.)
I am attempting to execute the following within python:
from pandas import *
tickdata = read_csv('/home/user/sourcefile.csv',index_col=0,parse_dates='TRUE')
The csv files has rows that look like:
2011/11/23 23:56:00.554389,1165.2500
2011/11/23 23:56:02.310943,1165.5000
2011/11/23 23:56:05.564009,1165.2500
On pandas .7, this executes fine. On pandas .8.0rc2, I get the error below. Because I have .7 and .8 installed on two different systems, I have not ruled out a dependency or python version difference. Any ideas on how to get this to execute under .8 are appreciated.
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python2.7/dist-packages/pandas-0.8.0rc2-py2.7-linux-x86_64.egg/pandas/io/parsers.py", line 225, in read_csv
return _read(TextParser, filepath_or_buffer, kwds)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.8.0rc2-py2.7-linux-x86_64.egg/pandas/io/parsers.py", line 192, in _read
return parser.get_chunk()
File "/usr/local/lib/python2.7/dist-packages/pandas-0.8.0rc2-py2.7-linux-x86_64.egg/pandas/io/parsers.py", line 728, in get_chunk
index = self._agg_index(index)
File "/usr/local/lib/python2.7/dist-packages/pandas-0.8.0rc2-py2.7-linux-x86_64.egg/pandas/io/parsers.py", line 846, in _agg_index
if try_parse_dates and self._should_parse_dates(self.index_col):
File "/usr/local/lib/python2.7/dist-packages/pandas-0.8.0rc2-py2.7-linux-x86_64.egg/pandas/io/parsers.py", line 874, in _should_parse_dates
return i in to_parse or name in to_parse
TypeError: 'in <string>' requires string as left operand, not int
I fixed the parser bug shown in the stack trace that you pasted. However, I'm wondering whether your date column is named "TRUE" or did you mean to just pass a boolean? I haven't dug through pandas history but I know that in 0.8 we are supporting much more complex date parsing behavior as part of the time series API so here we're interpreting the string value as a column name.
I've reported the bug on GitHub (best place for bug reports):
https://github.com/pydata/pandas/issues/1544
Should have a resolution today or tomorrow.