I'm doing sendex's machine learning tuturial on youtube and trying to replicate his code.
import pandas as pd
import numpy as np
import quandl
df = quandl.get("WIKI/GOOGL")
df = df.iloc[:,7:]
df['HL_PLT'] = (df['Adj. High'] - df['Adj. Low']) / df['Adj. High'] * 100
df['PCT_change'] = (df['Adj. Close'] - df['Adj. Open']) / df['Adj. Open'] * 100
I got an error
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-32-7b605dbbfdfe> in <module>
1 df = quandl.get("WIKI/GOOGL")
2 df = df.iloc[:,7:]
----> 3 df['HL_PLT'] = (df['Adj. High'] - df['Adj. Low']) / df['Adj. High'] * 100
4 df['PCT_change'] = (df['Adj. Close'] - df['Adj. Open']) / df['Adj. Open'] * 100
~\Anaconda3\lib\site-packages\pandas\core\frame.py in __setitem__(self, key, value)
3026
3027 # see if we can slice the rows
-> 3028 indexer = convert_to_index_sliceable(self, key)
3029 if indexer is not None:
3030 # either we have a slice or we have a string that can be converted
~\Anaconda3\lib\site-packages\pandas\core\indexing.py in convert_to_index_sliceable(obj, key)
2144 if idx._supports_partial_string_indexing:
2145 try:
-> 2146 return idx._get_string_slice(key)
2147 except (KeyError, ValueError, NotImplementedError):
2148 return None
~\Anaconda3\lib\site-packages\pandas\core\indexes\datetimes.py in _get_string_slice(self, key, use_lhs, use_rhs)
678 def _get_string_slice(self, key: str, use_lhs: bool = True, use_rhs: bool = True):
679 freq = getattr(self, "freqstr", getattr(self, "inferred_freq", None))
--> 680 parsed, reso = parsing.parse_time_string(key, freq)
681 reso = Resolution.from_attrname(reso)
682 loc = self._partial_date_slice(reso, parsed, use_lhs=use_lhs, use_rhs=use_rhs)
pandas\_libs\tslibs\parsing.pyx in pandas._libs.tslibs.parsing.parse_time_string()
pandas\_libs\tslibs\parsing.pyx in pandas._libs.tslibs.parsing.parse_datetime_string_with_reso()
pandas\_libs\tslibs\parsing.pyx in pandas._libs.tslibs.parsing.dateutil_parse()
TypeError: 'NoneType' object is not iterable
What I really curious is, without creating the column, the code run just fine
(df['Adj. High'] - df['Adj. Low']) / df['Adj. High'] * 100
(df['Adj. Close'] - df['Adj. Open']) / df['Adj. Open'] * 100
Date
2004-08-19 0.324968
2004-08-20 7.227007
2004-08-23 -1.227880
2004-08-24 -5.726357
2004-08-25 1.183658
...
2018-03-21 0.130884
2018-03-22 -2.487014
2018-03-23 -2.360729
2018-03-26 0.332191
2018-03-27 -5.353887
Length: 3424, dtype: float64
Even with some variable name, the column still created successfully
df['1'] = (df['Adj. High'] - df['Adj. Low']) / df['Adj. High'] * 100
df['2'] = (df['Adj. Close'] - df['Adj. Open']) / df['Adj. Open'] * 100
df
Adj. Open Adj. High Adj. Low Adj. Close Adj. Volume 1 2
Date
2004-08-19 50.159839 52.191109 48.128568 50.322842 44659000.0 7.783971 0.324968
2004-08-20 50.661387 54.708881 50.405597 54.322689 22834300.0 7.865787 7.227007
2004-08-23 55.551482 56.915693 54.693835 54.869377 18256100.0 3.903772 -1.227880
2004-08-24 55.792225 55.972783 51.945350 52.597363 15247300.0 7.195341 -5.726357
2004-08-25 52.542193 54.167209 52.100830 53.164113 9188600.0 3.814815 1.183658
... ... ... ... ... ... ... ...
2018-03-21 1092.570000 1108.700000 1087.210000 1094.000000 1990515.0 1.938306 0.130884
2018-03-22 1080.010000 1083.920000 1049.640000 1053.150000 3418154.0 3.162595 -2.487014
2018-03-23 1051.370000 1066.780000 1024.870000 1026.550000 2413517.0 3.928645 -2.360729
2018-03-26 1050.600000 1059.270000 1010.580000 1054.090000 3272409.0 4.596562 0.332191
2018-03-27 1063.900000 1064.540000 997.620000 1006.940000 2940957.0 6.286283 -5.353887
3424 rows × 7 columns
Column name 'a' created just fine too, but 'b' or anything longer than that created an error.
'TypeError: 'NoneType' object is not iterable'
