質問編集履歴
3
再編集
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int64
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```
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やはりAの列でobjectが表示されてしまいます。
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（無料ダウンロードのデータだと効率よく使用できないようになっている、、？
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---
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```python
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#pandasとnumpyをインポート
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import pandas as pd
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2
私的頂いた内容に沿っての修正
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[参考ページ](https://qiita.com/kazama1209/items/98f63b624f3987ba3322)
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[csvファイル参照](http://www.histdata.com)
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---
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以下、ppaulさんに回答頂いた内容について実践してみた内容となります。
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どこかに数字ではないデータが入っている可能性があるということで、
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ppaulさんに私的頂いたdtypeにてデータ型の確認を行ったところ、
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以下のような結果が得られました。
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```python
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print(dataM1['Open'].dtype)
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print(dataM1['High'].dtype)
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print(dataM1['Low'].dtype)
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print(dataM1['Close'].dtype)
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print(dataM1['A'].dtype)
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print(dataM1['f'].dtype)
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float64
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float64
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float64
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float64
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object
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int64
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```
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この結果より、Aの列に数字でないデータが入っている可能性があるということで、
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ヒストリカルデータを別のサイトから取得し、同様に実行してみました。
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[参照データ](https://www.axiory.com/jp/how-to-install/mt4-historical-data)
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以下dtypeでの実行結果です。
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```python
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print(dataM1['Open'].dtype)
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print(dataM1['High'].dtype)
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print(dataM1['Low'].dtype)
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print(dataM1['Close'].dtype)
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print(dataM1['A'].dtype)
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print(dataM1['f'].dtype)
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float64
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float64
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float64
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float64
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object
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int64
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```
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やはりAの列でobjectが表示されてしまいます。
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```python
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#pandasとnumpyをインポート
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import pandas as pd
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import numpy as np
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1
CSVファイルを参照したURLのリンクと、エラー内容の全文を掲載させて頂きました。（エラー内容長文となってしまい、申し訳ありません。）よろしくお願い致します。
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よろしくお願いいたします。
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[参考ページ](https://qiita.com/kazama1209/items/98f63b624f3987ba3322)
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[csvファイル参照](http://www.histdata.com)
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```python
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---> 11 ohlc_1h = make_mtf_ohlc(dataM1, '1H')
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12 ohlc_1h
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<ipython-input-4-5bc21324f74e> in make_mtf_ohlc(dataM1, tf)
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1 def make_mtf_ohlc(dataM1, tf):
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----> 2 x = dataM1.resample(tf).ohlc()
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3 O = x['Open']['open']
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4 H = x['High']['high']
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5 L = x['Low']['low']
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~/anaconda3/lib/python3.7/site-packages/pandas/core/resample.py in f(self, _method, *args, **kwargs)
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863 def f(self, _method=method, *args, **kwargs):
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864 nv.validate_resampler_func(_method, args, kwargs)
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--> 865 return self._downsample(_method)
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866 f.__doc__ = getattr(GroupBy, method).__doc__
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867 setattr(Resampler, method, f)
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~/anaconda3/lib/python3.7/site-packages/pandas/core/resample.py in _downsample(self, how, **kwargs)
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1018 # we want to call the actual grouper method here
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1019 result = obj.groupby(
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-> 1020 self.grouper, axis=self.axis).aggregate(how, **kwargs)
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1022 result = self._apply_loffset(result)
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~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/generic.py in aggregate(self, arg, *args, **kwargs)
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1313 @Appender(_shared_docs['aggregate'])
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1314 def aggregate(self, arg, *args, **kwargs):
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-> 1315 return super(DataFrameGroupBy, self).aggregate(arg, *args, **kwargs)
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1316
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1317 agg = aggregate
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~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/generic.py in aggregate(self, arg, *args, **kwargs)
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185 _level = kwargs.pop('_level', None)
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--> 186 result, how = self._aggregate(arg, _level=_level, *args, **kwargs)
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187 if how is None:
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188 return result
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~/anaconda3/lib/python3.7/site-packages/pandas/core/base.py in _aggregate(self, arg, *args, **kwargs)
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354 if isinstance(arg, compat.string_types):
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355 return self._try_aggregate_string_function(arg, *args,
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--> 356 **kwargs), None
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358 if isinstance(arg, dict):
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~/anaconda3/lib/python3.7/site-packages/pandas/core/base.py in _try_aggregate_string_function(self, arg, *args, **kwargs)
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310 if f is not None:
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311 if callable(f):
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--> 312 return f(*args, **kwargs)
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314 # people may try to aggregate on a non-callable attribute
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~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in ohlc(self)
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1318 return self._apply_to_column_groupbys(
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-> 1319 lambda x: x._cython_agg_general('ohlc'))
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1320
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1321 @Appender(DataFrame.describe.__doc__)
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~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/generic.py in _apply_to_column_groupbys(self, func)
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1490 (func(col_groupby) for _, col_groupby
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1491 in self._iterate_column_groupbys()),
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-> 1492 keys=self._selected_obj.columns, axis=1)
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1494 def _fill(self, direction, limit=None):
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~/anaconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, sort, copy)
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226 keys=keys, levels=levels, names=names,
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227 verify_integrity=verify_integrity,
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--> 228 copy=copy, sort=sort)
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229 return op.get_result()
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~/anaconda3/lib/python3.7/site-packages/pandas/core/reshape/concat.py in __init__(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy, sort)
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257 objs = [objs[k] for k in keys]
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258 else:
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--> 259 objs = list(objs)
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261 if len(objs) == 0:
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~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/generic.py in <genexpr>(.0)
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1488 from pandas.core.reshape.concat import concat
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1489 return concat(
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-> 1490 (func(col_groupby) for _, col_groupby
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1491 in self._iterate_column_groupbys()),
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1492 keys=self._selected_obj.columns, axis=1)
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~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in <lambda>(x)
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1317
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1318 return self._apply_to_column_groupbys(
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-> 1319 lambda x: x._cython_agg_general('ohlc'))
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1320
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1321 @Appender(DataFrame.describe.__doc__)
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~/anaconda3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in _cython_agg_general(self, how, alt, numeric_only, min_count)
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836
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837 if len(output) == 0:
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--> 838 raise DataError('No numeric types to aggregate')
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839
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840 return self._wrap_aggregated_output(output, names)
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DataError: No numeric types to aggregate
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```
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