Mixing
Concat DataFrame Reindexing only valid with uniquely valued Index objects
9
I am trying to concat the following:
df1
price side timestamp
timestamp
2016-01-04 00:01:15.631331072 0.7286 2 1451865675631331
2016-01-04 00:01:15.631399936 0.7286 2 1451865675631400
2016-01-04 00:01:15.631860992 0.7286 2 1451865675631861
2016-01-04 00:01:15.631866112 0.7286 2 1451865675631866
and
df2
bid bid_size offer offer_size
timestamp
2016-01-04 00:00:31.331441920 0.7284 4000000 0.7285 1000000
2016-01-04 00:00:53.631324928 0.7284 4000000 0.7290 4000000
2016-01-04 00:01:03.131234048 0.7284 5000000 0.7286 4000000
2016-01-04 00:01:12.131444992 0.7285 1000000 0.7286 4000000
2016-01-04 00:01:15.631364096 0.7285 4000000 0.7290 4000000
With
data = pd.concat([df1,df2], axis=1)
But I get the follwing output:
InvalidIndexError Traceback (most recent call last)
<ipython-input-38-2e88458f01d7> in <module>()
----> 1 data = pd.concat([df1,df2], axis=1)
2 data = data.fillna(method='pad')
3 data = data.fillna(method='bfill')
4 data['timestamp'] = data.index.values#converting to datetime
5 data['timestamp'] = pd.to_datetime(data['timestamp'])#converting to datetime
/usr/local/lib/python2.7/site-packages/pandas/tools/merge.pyc in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)
810 keys=keys, levels=levels, names=names,
811 verify_integrity=verify_integrity,
--> 812 copy=copy)
813 return op.get_result()
814
/usr/local/lib/python2.7/site-packages/pandas/tools/merge.pyc in __init__(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy)
947 self.copy = copy
948
--> 949 self.new_axes = self._get_new_axes()
950
951 def get_result(self):
/usr/local/lib/python2.7/site-packages/pandas/tools/merge.pyc in _get_new_axes(self)
1013 if i == self.axis:
1014 continue
-> 1015 new_axes[i] = self._get_comb_axis(i)
1016 else:
1017 if len(self.join_axes) != ndim - 1:
/usr/local/lib/python2.7/site-packages/pandas/tools/merge.pyc in _get_comb_axis(self, i)
1039 raise TypeError("Cannot concatenate list of %s" % types)
1040
-> 1041 return _get_combined_index(all_indexes, intersect=self.intersect)
1042
1043 def _get_concat_axis(self):
/usr/local/lib/python2.7/site-packages/pandas/core/index.pyc in _get_combined_index(indexes, intersect)
6120 index = index.intersection(other)
6121 return index
-> 6122 union = _union_indexes(indexes)
6123 return _ensure_index(union)
6124
/usr/local/lib/python2.7/site-packages/pandas/core/index.pyc in _union_indexes(indexes)
6149
6150 if hasattr(result, 'union_many'):
-> 6151 return result.union_many(indexes[1:])
6152 else:
6153 for other in indexes[1:]:
/usr/local/lib/python2.7/site-packages/pandas/tseries/index.pyc in union_many(self, others)
959 else:
960 tz = this.tz
--> 961 this = Index.union(this, other)
962 if isinstance(this, DatetimeIndex):
963 this.tz = tz
/usr/local/lib/python2.7/site-packages/pandas/core/index.pyc in union(self, other)
1553 result.extend([x for x in other._values if x not in value_set])
1554 else:
-> 1555 indexer = self.get_indexer(other)
1556 indexer, = (indexer == -1).nonzero()
1557
/usr/local/lib/python2.7/site-packages/pandas/core/index.pyc in get_indexer(self, target, method, limit, tolerance)
1890
1891 if not self.is_unique:
-> 1892 raise InvalidIndexError('Reindexing only valid with uniquely'
1893 ' valued Index objects')
1894
InvalidIndexError: Reindexing only valid with uniquely valued Index objects
I have removed additional columns and removed duplicates and NA where there could be a conflict - but I simply do not know whats wrong.
Please help
Thanks
python numpy pandas
asked Jan 29 '16 at 12:00
noideanoidea
89227
add a comment |
9
I am trying to concat the following:
df1
price side timestamp
timestamp
2016-01-04 00:01:15.631331072 0.7286 2 1451865675631331
2016-01-04 00:01:15.631399936 0.7286 2 1451865675631400
2016-01-04 00:01:15.631860992 0.7286 2 1451865675631861
2016-01-04 00:01:15.631866112 0.7286 2 1451865675631866
and
df2
bid bid_size offer offer_size
timestamp
2016-01-04 00:00:31.331441920 0.7284 4000000 0.7285 1000000
2016-01-04 00:00:53.631324928 0.7284 4000000 0.7290 4000000
2016-01-04 00:01:03.131234048 0.7284 5000000 0.7286 4000000
2016-01-04 00:01:12.131444992 0.7285 1000000 0.7286 4000000
2016-01-04 00:01:15.631364096 0.7285 4000000 0.7290 4000000
With
data = pd.concat([df1,df2], axis=1)
But I get the follwing output:
InvalidIndexError Traceback (most recent call last)
<ipython-input-38-2e88458f01d7> in <module>()
----> 1 data = pd.concat([df1,df2], axis=1)
2 data = data.fillna(method='pad')
3 data = data.fillna(method='bfill')
4 data['timestamp'] = data.index.values#converting to datetime
5 data['timestamp'] = pd.to_datetime(data['timestamp'])#converting to datetime
/usr/local/lib/python2.7/site-packages/pandas/tools/merge.pyc in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)
810 keys=keys, levels=levels, names=names,
811 verify_integrity=verify_integrity,
--> 812 copy=copy)
813 return op.get_result()
814
/usr/local/lib/python2.7/site-packages/pandas/tools/merge.pyc in __init__(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy)
947 self.copy = copy
948
--> 949 self.new_axes = self._get_new_axes()
950
951 def get_result(self):
/usr/local/lib/python2.7/site-packages/pandas/tools/merge.pyc in _get_new_axes(self)
1013 if i == self.axis:
1014 continue
-> 1015 new_axes[i] = self._get_comb_axis(i)
1016 else:
1017 if len(self.join_axes) != ndim - 1:
/usr/local/lib/python2.7/site-packages/pandas/tools/merge.pyc in _get_comb_axis(self, i)
1039 raise TypeError("Cannot concatenate list of %s" % types)
1040
-> 1041 return _get_combined_index(all_indexes, intersect=self.intersect)
1042
1043 def _get_concat_axis(self):
/usr/local/lib/python2.7/site-packages/pandas/core/index.pyc in _get_combined_index(indexes, intersect)
6120 index = index.intersection(other)
6121 return index
-> 6122 union = _union_indexes(indexes)
6123 return _ensure_index(union)
6124
/usr/local/lib/python2.7/site-packages/pandas/core/index.pyc in _union_indexes(indexes)
6149
6150 if hasattr(result, 'union_many'):
-> 6151 return result.union_many(indexes[1:])
6152 else:
6153 for other in indexes[1:]:
/usr/local/lib/python2.7/site-packages/pandas/tseries/index.pyc in union_many(self, others)
959 else:
960 tz = this.tz
--> 961 this = Index.union(this, other)
962 if isinstance(this, DatetimeIndex):
963 this.tz = tz
/usr/local/lib/python2.7/site-packages/pandas/core/index.pyc in union(self, other)
1553 result.extend([x for x in other._values if x not in value_set])
1554 else:
-> 1555 indexer = self.get_indexer(other)
1556 indexer, = (indexer == -1).nonzero()
1557
/usr/local/lib/python2.7/site-packages/pandas/core/index.pyc in get_indexer(self, target, method, limit, tolerance)
1890
1891 if not self.is_unique:
-> 1892 raise InvalidIndexError('Reindexing only valid with uniquely'
1893 ' valued Index objects')
1894
InvalidIndexError: Reindexing only valid with uniquely valued Index objects
I have removed additional columns and removed duplicates and NA where there could be a conflict - but I simply do not know whats wrong.
Please help
Thanks
python numpy pandas
asked Jan 29 '16 at 12:00
noideanoidea
89227
what doespd.concatdo?
– gmoshkin
Jan 29 '16 at 12:05
@gmoshkin it places dataframes together as one dataframe - joined on the axis.
– noidea
Jan 29 '16 at 12:29
I have removed the column timestamp from both df1 and df2 and attempted to drop and NA with df1.dropna() and df2.dropna(); The problem persists....
– noidea
Jan 29 '16 at 12:49
I do not understand what neitherpdnordf1anddf2are. so I have no Idea neither what you are trying to do nor what goes wrong.
– gmoshkin
Jan 29 '16 at 14:36
1
@gmoshkin, I am assuming that pd as an alias for pandas: import pandas as pd, and df1 and df2 are pandas DataFrame objects
– jugovich
Nov 16 '17 at 2:30
add a comment |
9
9
9
2
I am trying to concat the following:
df1
price side timestamp
timestamp
2016-01-04 00:01:15.631331072 0.7286 2 1451865675631331
2016-01-04 00:01:15.631399936 0.7286 2 1451865675631400
2016-01-04 00:01:15.631860992 0.7286 2 1451865675631861
2016-01-04 00:01:15.631866112 0.7286 2 1451865675631866
and
df2
bid bid_size offer offer_size
timestamp
2016-01-04 00:00:31.331441920 0.7284 4000000 0.7285 1000000
2016-01-04 00:00:53.631324928 0.7284 4000000 0.7290 4000000
2016-01-04 00:01:03.131234048 0.7284 5000000 0.7286 4000000
2016-01-04 00:01:12.131444992 0.7285 1000000 0.7286 4000000
2016-01-04 00:01:15.631364096 0.7285 4000000 0.7290 4000000
With
data = pd.concat([df1,df2], axis=1)
But I get the follwing output:
InvalidIndexError Traceback (most recent call last)
<ipython-input-38-2e88458f01d7> in <module>()
----> 1 data = pd.concat([df1,df2], axis=1)
2 data = data.fillna(method='pad')
3 data = data.fillna(method='bfill')
4 data['timestamp'] = data.index.values#converting to datetime
5 data['timestamp'] = pd.to_datetime(data['timestamp'])#converting to datetime
/usr/local/lib/python2.7/site-packages/pandas/tools/merge.pyc in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)
810 keys=keys, levels=levels, names=names,
811 verify_integrity=verify_integrity,
--> 812 copy=copy)
813 return op.get_result()
814
/usr/local/lib/python2.7/site-packages/pandas/tools/merge.pyc in __init__(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy)
947 self.copy = copy
948
--> 949 self.new_axes = self._get_new_axes()
950
951 def get_result(self):
/usr/local/lib/python2.7/site-packages/pandas/tools/merge.pyc in _get_new_axes(self)
1013 if i == self.axis:
1014 continue
-> 1015 new_axes[i] = self._get_comb_axis(i)
1016 else:
1017 if len(self.join_axes) != ndim - 1:
/usr/local/lib/python2.7/site-packages/pandas/tools/merge.pyc in _get_comb_axis(self, i)
1039 raise TypeError("Cannot concatenate list of %s" % types)
1040
-> 1041 return _get_combined_index(all_indexes, intersect=self.intersect)
1042
1043 def _get_concat_axis(self):
/usr/local/lib/python2.7/site-packages/pandas/core/index.pyc in _get_combined_index(indexes, intersect)
6120 index = index.intersection(other)
6121 return index
-> 6122 union = _union_indexes(indexes)
6123 return _ensure_index(union)
6124
/usr/local/lib/python2.7/site-packages/pandas/core/index.pyc in _union_indexes(indexes)
6149
6150 if hasattr(result, 'union_many'):
-> 6151 return result.union_many(indexes[1:])
6152 else:
6153 for other in indexes[1:]:
/usr/local/lib/python2.7/site-packages/pandas/tseries/index.pyc in union_many(self, others)
959 else:
960 tz = this.tz
--> 961 this = Index.union(this, other)
962 if isinstance(this, DatetimeIndex):
963 this.tz = tz
/usr/local/lib/python2.7/site-packages/pandas/core/index.pyc in union(self, other)
1553 result.extend([x for x in other._values if x not in value_set])
1554 else:
-> 1555 indexer = self.get_indexer(other)
1556 indexer, = (indexer == -1).nonzero()
1557
/usr/local/lib/python2.7/site-packages/pandas/core/index.pyc in get_indexer(self, target, method, limit, tolerance)
1890
1891 if not self.is_unique:
-> 1892 raise InvalidIndexError('Reindexing only valid with uniquely'
1893 ' valued Index objects')
1894
InvalidIndexError: Reindexing only valid with uniquely valued Index objects
I have removed additional columns and removed duplicates and NA where there could be a conflict - but I simply do not know whats wrong.
Please help
Thanks
python numpy pandas
asked Jan 29 '16 at 12:00
noideanoidea
89227
I am trying to concat the following:
df1
price side timestamp
timestamp
2016-01-04 00:01:15.631331072 0.7286 2 1451865675631331
2016-01-04 00:01:15.631399936 0.7286 2 1451865675631400
2016-01-04 00:01:15.631860992 0.7286 2 1451865675631861
2016-01-04 00:01:15.631866112 0.7286 2 1451865675631866
and
df2
bid bid_size offer offer_size
timestamp
2016-01-04 00:00:31.331441920 0.7284 4000000 0.7285 1000000
2016-01-04 00:00:53.631324928 0.7284 4000000 0.7290 4000000
2016-01-04 00:01:03.131234048 0.7284 5000000 0.7286 4000000
2016-01-04 00:01:12.131444992 0.7285 1000000 0.7286 4000000
2016-01-04 00:01:15.631364096 0.7285 4000000 0.7290 4000000
With
data = pd.concat([df1,df2], axis=1)
But I get the follwing output:
InvalidIndexError Traceback (most recent call last)
<ipython-input-38-2e88458f01d7> in <module>()
----> 1 data = pd.concat([df1,df2], axis=1)
2 data = data.fillna(method='pad')
3 data = data.fillna(method='bfill')
4 data['timestamp'] = data.index.values#converting to datetime
5 data['timestamp'] = pd.to_datetime(data['timestamp'])#converting to datetime
/usr/local/lib/python2.7/site-packages/pandas/tools/merge.pyc in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)
810 keys=keys, levels=levels, names=names,
811 verify_integrity=verify_integrity,
--> 812 copy=copy)
813 return op.get_result()
814
/usr/local/lib/python2.7/site-packages/pandas/tools/merge.pyc in __init__(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy)
947 self.copy = copy
948
--> 949 self.new_axes = self._get_new_axes()
950
951 def get_result(self):
/usr/local/lib/python2.7/site-packages/pandas/tools/merge.pyc in _get_new_axes(self)
1013 if i == self.axis:
1014 continue
-> 1015 new_axes[i] = self._get_comb_axis(i)
1016 else:
1017 if len(self.join_axes) != ndim - 1:
/usr/local/lib/python2.7/site-packages/pandas/tools/merge.pyc in _get_comb_axis(self, i)
1039 raise TypeError("Cannot concatenate list of %s" % types)
1040
-> 1041 return _get_combined_index(all_indexes, intersect=self.intersect)
1042
1043 def _get_concat_axis(self):
/usr/local/lib/python2.7/site-packages/pandas/core/index.pyc in _get_combined_index(indexes, intersect)
6120 index = index.intersection(other)
6121 return index
-> 6122 union = _union_indexes(indexes)
6123 return _ensure_index(union)
6124
/usr/local/lib/python2.7/site-packages/pandas/core/index.pyc in _union_indexes(indexes)
6149
6150 if hasattr(result, 'union_many'):
-> 6151 return result.union_many(indexes[1:])
6152 else:
6153 for other in indexes[1:]:
/usr/local/lib/python2.7/site-packages/pandas/tseries/index.pyc in union_many(self, others)
959 else:
960 tz = this.tz
--> 961 this = Index.union(this, other)
962 if isinstance(this, DatetimeIndex):
963 this.tz = tz
/usr/local/lib/python2.7/site-packages/pandas/core/index.pyc in union(self, other)
1553 result.extend([x for x in other._values if x not in value_set])
1554 else:
-> 1555 indexer = self.get_indexer(other)
1556 indexer, = (indexer == -1).nonzero()
1557
/usr/local/lib/python2.7/site-packages/pandas/core/index.pyc in get_indexer(self, target, method, limit, tolerance)
1890
1891 if not self.is_unique:
-> 1892 raise InvalidIndexError('Reindexing only valid with uniquely'
1893 ' valued Index objects')
1894
InvalidIndexError: Reindexing only valid with uniquely valued Index objects
I have removed additional columns and removed duplicates and NA where there could be a conflict - but I simply do not know whats wrong.
Please help
Thanks
python numpy pandas
python numpy pandas
asked Jan 29 '16 at 12:00
noideanoidea
89227
asked Jan 29 '16 at 12:00
noideanoidea
89227
edited Jan 29 '16 at 13:56
noidea
asked Jan 29 '16 at 12:00
noideanoidea
89227
asked Jan 29 '16 at 12:00
noideanoidea
89227
asked Jan 29 '16 at 12:00
noideanoidea
89227
89227
what doespd.concatdo?
– gmoshkin
Jan 29 '16 at 12:05
@gmoshkin it places dataframes together as one dataframe - joined on the axis.
– noidea
Jan 29 '16 at 12:29
I have removed the column timestamp from both df1 and df2 and attempted to drop and NA with df1.dropna() and df2.dropna(); The problem persists....
– noidea
Jan 29 '16 at 12:49
I do not understand what neitherpdnordf1anddf2are. so I have no Idea neither what you are trying to do nor what goes wrong.
– gmoshkin
Jan 29 '16 at 14:36
1
@gmoshkin, I am assuming that pd as an alias for pandas: import pandas as pd, and df1 and df2 are pandas DataFrame objects
– jugovich
Nov 16 '17 at 2:30
add a comment |
what doespd.concatdo?
– gmoshkin
Jan 29 '16 at 12:05
@gmoshkin it places dataframes together as one dataframe - joined on the axis.
– noidea
Jan 29 '16 at 12:29
I have removed the column timestamp from both df1 and df2 and attempted to drop and NA with df1.dropna() and df2.dropna(); The problem persists....
– noidea
Jan 29 '16 at 12:49
I do not understand what neitherpdnordf1anddf2are. so I have no Idea neither what you are trying to do nor what goes wrong.
– gmoshkin
Jan 29 '16 at 14:36
1
@gmoshkin, I am assuming that pd as an alias for pandas: import pandas as pd, and df1 and df2 are pandas DataFrame objects
– jugovich
Nov 16 '17 at 2:30
what does
pd.concat do?– gmoshkin
Jan 29 '16 at 12:05
what does
pd.concat do?– gmoshkin
Jan 29 '16 at 12:05
@gmoshkin it places dataframes together as one dataframe - joined on the axis.
– noidea
Jan 29 '16 at 12:29
@gmoshkin it places dataframes together as one dataframe - joined on the axis.
– noidea
Jan 29 '16 at 12:29
I have removed the column timestamp from both df1 and df2 and attempted to drop and NA with df1.dropna() and df2.dropna(); The problem persists....
– noidea
Jan 29 '16 at 12:49
I have removed the column timestamp from both df1 and df2 and attempted to drop and NA with df1.dropna() and df2.dropna(); The problem persists....
– noidea
Jan 29 '16 at 12:49
I do not understand what neither
pd nor df1 and df2 are. so I have no Idea neither what you are trying to do nor what goes wrong.– gmoshkin
Jan 29 '16 at 14:36
I do not understand what neither
pd nor df1 and df2 are. so I have no Idea neither what you are trying to do nor what goes wrong.– gmoshkin
Jan 29 '16 at 14:36
1
1
@gmoshkin, I am assuming that pd as an alias for pandas: import pandas as pd, and df1 and df2 are pandas DataFrame objects
– jugovich
Nov 16 '17 at 2:30
@gmoshkin, I am assuming that pd as an alias for pandas: import pandas as pd, and df1 and df2 are pandas DataFrame objects
– jugovich
Nov 16 '17 at 2:30
add a comment |
3 Answers
3
active
oldest
votes
24
pd.concat requires that the indices be unique. To remove rows with duplicate indices, use
df = df.loc[~df.index.duplicated(keep='first')]
import pandas as pd
from pandas import Timestamp
df1 = pd.DataFrame(
{'price': [0.7286, 0.7286, 0.7286, 0.7286],
'side': [2, 2, 2, 2],
'timestamp': [1451865675631331, 1451865675631400,
1451865675631861, 1451865675631866]},
index=pd.DatetimeIndex(['2000-1-1', '2000-1-1', '2001-1-1', '2002-1-1']))
df2 = pd.DataFrame(
{'bid': [0.7284, 0.7284, 0.7284, 0.7285, 0.7285],
'bid_size': [4000000, 4000000, 5000000, 1000000, 4000000],
'offer': [0.7285, 0.729, 0.7286, 0.7286, 0.729],
'offer_size': [1000000, 4000000, 4000000, 4000000, 4000000]},
index=pd.DatetimeIndex(['2000-1-1', '2001-1-1', '2002-1-1', '2003-1-1', '2004-1-1']))
df1 = df1.loc[~df1.index.duplicated(keep='first')]
# price side timestamp
# 2000-01-01 0.7286 2 1451865675631331
# 2001-01-01 0.7286 2 1451865675631861
# 2002-01-01 0.7286 2 1451865675631866
df2 = df2.loc[~df2.index.duplicated(keep='first')]
# bid bid_size offer offer_size
# 2000-01-01 0.7284 4000000 0.7285 1000000
# 2001-01-01 0.7284 4000000 0.7290 4000000
# 2002-01-01 0.7284 5000000 0.7286 4000000
# 2003-01-01 0.7285 1000000 0.7286 4000000
# 2004-01-01 0.7285 4000000 0.7290 4000000
result = pd.concat([df1, df2], axis=0)
print(result)
bid bid_size offer offer_size price side timestamp
2000-01-01 NaN NaN NaN NaN 0.7286 2 1.451866e+15
2001-01-01 NaN NaN NaN NaN 0.7286 2 1.451866e+15
2002-01-01 NaN NaN NaN NaN 0.7286 2 1.451866e+15
2000-01-01 0.7284 4000000 0.7285 1000000 NaN NaN NaN
2001-01-01 0.7284 4000000 0.7290 4000000 NaN NaN NaN
2002-01-01 0.7284 5000000 0.7286 4000000 NaN NaN NaN
2003-01-01 0.7285 1000000 0.7286 4000000 NaN NaN NaN
2004-01-01 0.7285 4000000 0.7290 4000000 NaN NaN NaN
Note there is also pd.join, which can join DataFrames based on their indices,
and handle non-unique indices based on the how parameter. Rows with duplicate
index are not removed.
In [94]: df1.join(df2)
Out[94]:
price side timestamp bid bid_size offer
2000-01-01 0.7286 2 1451865675631331 0.7284 4000000 0.7285
2000-01-01 0.7286 2 1451865675631400 0.7284 4000000 0.7285
2001-01-01 0.7286 2 1451865675631861 0.7284 4000000 0.7290
2002-01-01 0.7286 2 1451865675631866 0.7284 5000000 0.7286
offer_size
2000-01-01 1000000
2000-01-01 1000000
2001-01-01 4000000
2002-01-01 4000000
In [95]: df1.join(df2, how='outer')
Out[95]:
price side timestamp bid bid_size offer offer_size
2000-01-01 0.7286 2 1.451866e+15 0.7284 4000000 0.7285 1000000
2000-01-01 0.7286 2 1.451866e+15 0.7284 4000000 0.7285 1000000
2001-01-01 0.7286 2 1.451866e+15 0.7284 4000000 0.7290 4000000
2002-01-01 0.7286 2 1.451866e+15 0.7284 5000000 0.7286 4000000
2003-01-01 NaN NaN NaN 0.7285 1000000 0.7286 4000000
2004-01-01 NaN NaN NaN 0.7285 4000000 0.7290 4000000
edited May 23 '17 at 12:34
Community♦
11
answered Jan 29 '16 at 15:26
unutbuunutbu
550k10111821239
@untubu - works a treat with join Thanks
– noidea
Jan 29 '16 at 17:30
add a comment |
10
You can mitigate this error without having to change your data or remove duplicates. Just create a new index with DataFrame.reset_index:
df = df.reset_index()
The old index is kept as a column in your dataframe, but if you don't need it you can do:
df = df.reset_index(drop=True)
Some prefer:
df.reset_index(inplace=True, drop=True)
answered Jul 12 '17 at 11:16
Nicholas MorleyNicholas Morley
1,316279
1
This should be marked asd the right answer, since it solves the problem without loosing information. Not always duplicate records are wrong on the datasets.
– Juanu Haedo
Nov 24 '17 at 19:29
add a comment |
0
Another thing that might throw this type of errors is when you have a column with a unique value inside (entropy of 0).
In this case, you can either inject small Gaussian noise in that column or remove it completely.
answered Nov 21 '18 at 10:31
May Pilijay ElMay Pilijay El
113
add a comment |
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24
pd.concat requires that the indices be unique. To remove rows with duplicate indices, use
df = df.loc[~df.index.duplicated(keep='first')]
import pandas as pd
from pandas import Timestamp
df1 = pd.DataFrame(
{'price': [0.7286, 0.7286, 0.7286, 0.7286],
'side': [2, 2, 2, 2],
'timestamp': [1451865675631331, 1451865675631400,
1451865675631861, 1451865675631866]},
index=pd.DatetimeIndex(['2000-1-1', '2000-1-1', '2001-1-1', '2002-1-1']))
df2 = pd.DataFrame(
{'bid': [0.7284, 0.7284, 0.7284, 0.7285, 0.7285],
'bid_size': [4000000, 4000000, 5000000, 1000000, 4000000],
'offer': [0.7285, 0.729, 0.7286, 0.7286, 0.729],
'offer_size': [1000000, 4000000, 4000000, 4000000, 4000000]},
index=pd.DatetimeIndex(['2000-1-1', '2001-1-1', '2002-1-1', '2003-1-1', '2004-1-1']))
df1 = df1.loc[~df1.index.duplicated(keep='first')]
# price side timestamp
# 2000-01-01 0.7286 2 1451865675631331
# 2001-01-01 0.7286 2 1451865675631861
# 2002-01-01 0.7286 2 1451865675631866
df2 = df2.loc[~df2.index.duplicated(keep='first')]
# bid bid_size offer offer_size
# 2000-01-01 0.7284 4000000 0.7285 1000000
# 2001-01-01 0.7284 4000000 0.7290 4000000
# 2002-01-01 0.7284 5000000 0.7286 4000000
# 2003-01-01 0.7285 1000000 0.7286 4000000
# 2004-01-01 0.7285 4000000 0.7290 4000000
result = pd.concat([df1, df2], axis=0)
print(result)
bid bid_size offer offer_size price side timestamp
2000-01-01 NaN NaN NaN NaN 0.7286 2 1.451866e+15
2001-01-01 NaN NaN NaN NaN 0.7286 2 1.451866e+15
2002-01-01 NaN NaN NaN NaN 0.7286 2 1.451866e+15
2000-01-01 0.7284 4000000 0.7285 1000000 NaN NaN NaN
2001-01-01 0.7284 4000000 0.7290 4000000 NaN NaN NaN
2002-01-01 0.7284 5000000 0.7286 4000000 NaN NaN NaN
2003-01-01 0.7285 1000000 0.7286 4000000 NaN NaN NaN
2004-01-01 0.7285 4000000 0.7290 4000000 NaN NaN NaN
Note there is also pd.join, which can join DataFrames based on their indices,
and handle non-unique indices based on the how parameter. Rows with duplicate
index are not removed.
In [94]: df1.join(df2)
Out[94]:
price side timestamp bid bid_size offer
2000-01-01 0.7286 2 1451865675631331 0.7284 4000000 0.7285
2000-01-01 0.7286 2 1451865675631400 0.7284 4000000 0.7285
2001-01-01 0.7286 2 1451865675631861 0.7284 4000000 0.7290
2002-01-01 0.7286 2 1451865675631866 0.7284 5000000 0.7286
offer_size
2000-01-01 1000000
2000-01-01 1000000
2001-01-01 4000000
2002-01-01 4000000
In [95]: df1.join(df2, how='outer')
Out[95]:
price side timestamp bid bid_size offer offer_size
2000-01-01 0.7286 2 1.451866e+15 0.7284 4000000 0.7285 1000000
2000-01-01 0.7286 2 1.451866e+15 0.7284 4000000 0.7285 1000000
2001-01-01 0.7286 2 1.451866e+15 0.7284 4000000 0.7290 4000000
2002-01-01 0.7286 2 1.451866e+15 0.7284 5000000 0.7286 4000000
2003-01-01 NaN NaN NaN 0.7285 1000000 0.7286 4000000
2004-01-01 NaN NaN NaN 0.7285 4000000 0.7290 4000000
edited May 23 '17 at 12:34
Community♦
11
answered Jan 29 '16 at 15:26
unutbuunutbu
550k10111821239
@untubu - works a treat with join Thanks
– noidea
Jan 29 '16 at 17:30
add a comment |
24
pd.concat requires that the indices be unique. To remove rows with duplicate indices, use
df = df.loc[~df.index.duplicated(keep='first')]
import pandas as pd
from pandas import Timestamp
df1 = pd.DataFrame(
{'price': [0.7286, 0.7286, 0.7286, 0.7286],
'side': [2, 2, 2, 2],
'timestamp': [1451865675631331, 1451865675631400,
1451865675631861, 1451865675631866]},
index=pd.DatetimeIndex(['2000-1-1', '2000-1-1', '2001-1-1', '2002-1-1']))
df2 = pd.DataFrame(
{'bid': [0.7284, 0.7284, 0.7284, 0.7285, 0.7285],
'bid_size': [4000000, 4000000, 5000000, 1000000, 4000000],
'offer': [0.7285, 0.729, 0.7286, 0.7286, 0.729],
'offer_size': [1000000, 4000000, 4000000, 4000000, 4000000]},
index=pd.DatetimeIndex(['2000-1-1', '2001-1-1', '2002-1-1', '2003-1-1', '2004-1-1']))
df1 = df1.loc[~df1.index.duplicated(keep='first')]
# price side timestamp
# 2000-01-01 0.7286 2 1451865675631331
# 2001-01-01 0.7286 2 1451865675631861
# 2002-01-01 0.7286 2 1451865675631866
df2 = df2.loc[~df2.index.duplicated(keep='first')]
# bid bid_size offer offer_size
# 2000-01-01 0.7284 4000000 0.7285 1000000
# 2001-01-01 0.7284 4000000 0.7290 4000000
# 2002-01-01 0.7284 5000000 0.7286 4000000
# 2003-01-01 0.7285 1000000 0.7286 4000000
# 2004-01-01 0.7285 4000000 0.7290 4000000
result = pd.concat([df1, df2], axis=0)
print(result)
bid bid_size offer offer_size price side timestamp
2000-01-01 NaN NaN NaN NaN 0.7286 2 1.451866e+15
2001-01-01 NaN NaN NaN NaN 0.7286 2 1.451866e+15
2002-01-01 NaN NaN NaN NaN 0.7286 2 1.451866e+15
2000-01-01 0.7284 4000000 0.7285 1000000 NaN NaN NaN
2001-01-01 0.7284 4000000 0.7290 4000000 NaN NaN NaN
2002-01-01 0.7284 5000000 0.7286 4000000 NaN NaN NaN
2003-01-01 0.7285 1000000 0.7286 4000000 NaN NaN NaN
2004-01-01 0.7285 4000000 0.7290 4000000 NaN NaN NaN
Note there is also pd.join, which can join DataFrames based on their indices,
and handle non-unique indices based on the how parameter. Rows with duplicate
index are not removed.
In [94]: df1.join(df2)
Out[94]:
price side timestamp bid bid_size offer
2000-01-01 0.7286 2 1451865675631331 0.7284 4000000 0.7285
2000-01-01 0.7286 2 1451865675631400 0.7284 4000000 0.7285
2001-01-01 0.7286 2 1451865675631861 0.7284 4000000 0.7290
2002-01-01 0.7286 2 1451865675631866 0.7284 5000000 0.7286
offer_size
2000-01-01 1000000
2000-01-01 1000000
2001-01-01 4000000
2002-01-01 4000000
In [95]: df1.join(df2, how='outer')
Out[95]:
price side timestamp bid bid_size offer offer_size
2000-01-01 0.7286 2 1.451866e+15 0.7284 4000000 0.7285 1000000
2000-01-01 0.7286 2 1.451866e+15 0.7284 4000000 0.7285 1000000
2001-01-01 0.7286 2 1.451866e+15 0.7284 4000000 0.7290 4000000
2002-01-01 0.7286 2 1.451866e+15 0.7284 5000000 0.7286 4000000
2003-01-01 NaN NaN NaN 0.7285 1000000 0.7286 4000000
2004-01-01 NaN NaN NaN 0.7285 4000000 0.7290 4000000
edited May 23 '17 at 12:34
Community♦
11
answered Jan 29 '16 at 15:26
unutbuunutbu
550k10111821239
@untubu - works a treat with join Thanks
– noidea
Jan 29 '16 at 17:30
add a comment |
24
24
24
pd.concat requires that the indices be unique. To remove rows with duplicate indices, use
df = df.loc[~df.index.duplicated(keep='first')]
import pandas as pd
from pandas import Timestamp
df1 = pd.DataFrame(
{'price': [0.7286, 0.7286, 0.7286, 0.7286],
'side': [2, 2, 2, 2],
'timestamp': [1451865675631331, 1451865675631400,
1451865675631861, 1451865675631866]},
index=pd.DatetimeIndex(['2000-1-1', '2000-1-1', '2001-1-1', '2002-1-1']))
df2 = pd.DataFrame(
{'bid': [0.7284, 0.7284, 0.7284, 0.7285, 0.7285],
'bid_size': [4000000, 4000000, 5000000, 1000000, 4000000],
'offer': [0.7285, 0.729, 0.7286, 0.7286, 0.729],
'offer_size': [1000000, 4000000, 4000000, 4000000, 4000000]},
index=pd.DatetimeIndex(['2000-1-1', '2001-1-1', '2002-1-1', '2003-1-1', '2004-1-1']))
df1 = df1.loc[~df1.index.duplicated(keep='first')]
# price side timestamp
# 2000-01-01 0.7286 2 1451865675631331
# 2001-01-01 0.7286 2 1451865675631861
# 2002-01-01 0.7286 2 1451865675631866
df2 = df2.loc[~df2.index.duplicated(keep='first')]
# bid bid_size offer offer_size
# 2000-01-01 0.7284 4000000 0.7285 1000000
# 2001-01-01 0.7284 4000000 0.7290 4000000
# 2002-01-01 0.7284 5000000 0.7286 4000000
# 2003-01-01 0.7285 1000000 0.7286 4000000
# 2004-01-01 0.7285 4000000 0.7290 4000000
result = pd.concat([df1, df2], axis=0)
print(result)
bid bid_size offer offer_size price side timestamp
2000-01-01 NaN NaN NaN NaN 0.7286 2 1.451866e+15
2001-01-01 NaN NaN NaN NaN 0.7286 2 1.451866e+15
2002-01-01 NaN NaN NaN NaN 0.7286 2 1.451866e+15
2000-01-01 0.7284 4000000 0.7285 1000000 NaN NaN NaN
2001-01-01 0.7284 4000000 0.7290 4000000 NaN NaN NaN
2002-01-01 0.7284 5000000 0.7286 4000000 NaN NaN NaN
2003-01-01 0.7285 1000000 0.7286 4000000 NaN NaN NaN
2004-01-01 0.7285 4000000 0.7290 4000000 NaN NaN NaN
Note there is also pd.join, which can join DataFrames based on their indices,
and handle non-unique indices based on the how parameter. Rows with duplicate
index are not removed.
In [94]: df1.join(df2)
Out[94]:
price side timestamp bid bid_size offer
2000-01-01 0.7286 2 1451865675631331 0.7284 4000000 0.7285
2000-01-01 0.7286 2 1451865675631400 0.7284 4000000 0.7285
2001-01-01 0.7286 2 1451865675631861 0.7284 4000000 0.7290
2002-01-01 0.7286 2 1451865675631866 0.7284 5000000 0.7286
offer_size
2000-01-01 1000000
2000-01-01 1000000
2001-01-01 4000000
2002-01-01 4000000
In [95]: df1.join(df2, how='outer')
Out[95]:
price side timestamp bid bid_size offer offer_size
2000-01-01 0.7286 2 1.451866e+15 0.7284 4000000 0.7285 1000000
2000-01-01 0.7286 2 1.451866e+15 0.7284 4000000 0.7285 1000000
2001-01-01 0.7286 2 1.451866e+15 0.7284 4000000 0.7290 4000000
2002-01-01 0.7286 2 1.451866e+15 0.7284 5000000 0.7286 4000000
2003-01-01 NaN NaN NaN 0.7285 1000000 0.7286 4000000
2004-01-01 NaN NaN NaN 0.7285 4000000 0.7290 4000000
edited May 23 '17 at 12:34
Community♦
11
answered Jan 29 '16 at 15:26
unutbuunutbu
550k10111821239
pd.concat requires that the indices be unique. To remove rows with duplicate indices, use
df = df.loc[~df.index.duplicated(keep='first')]
import pandas as pd
from pandas import Timestamp
df1 = pd.DataFrame(
{'price': [0.7286, 0.7286, 0.7286, 0.7286],
'side': [2, 2, 2, 2],
'timestamp': [1451865675631331, 1451865675631400,
1451865675631861, 1451865675631866]},
index=pd.DatetimeIndex(['2000-1-1', '2000-1-1', '2001-1-1', '2002-1-1']))
df2 = pd.DataFrame(
{'bid': [0.7284, 0.7284, 0.7284, 0.7285, 0.7285],
'bid_size': [4000000, 4000000, 5000000, 1000000, 4000000],
'offer': [0.7285, 0.729, 0.7286, 0.7286, 0.729],
'offer_size': [1000000, 4000000, 4000000, 4000000, 4000000]},
index=pd.DatetimeIndex(['2000-1-1', '2001-1-1', '2002-1-1', '2003-1-1', '2004-1-1']))
df1 = df1.loc[~df1.index.duplicated(keep='first')]
# price side timestamp
# 2000-01-01 0.7286 2 1451865675631331
# 2001-01-01 0.7286 2 1451865675631861
# 2002-01-01 0.7286 2 1451865675631866
df2 = df2.loc[~df2.index.duplicated(keep='first')]
# bid bid_size offer offer_size
# 2000-01-01 0.7284 4000000 0.7285 1000000
# 2001-01-01 0.7284 4000000 0.7290 4000000
# 2002-01-01 0.7284 5000000 0.7286 4000000
# 2003-01-01 0.7285 1000000 0.7286 4000000
# 2004-01-01 0.7285 4000000 0.7290 4000000
result = pd.concat([df1, df2], axis=0)
print(result)
bid bid_size offer offer_size price side timestamp
2000-01-01 NaN NaN NaN NaN 0.7286 2 1.451866e+15
2001-01-01 NaN NaN NaN NaN 0.7286 2 1.451866e+15
2002-01-01 NaN NaN NaN NaN 0.7286 2 1.451866e+15
2000-01-01 0.7284 4000000 0.7285 1000000 NaN NaN NaN
2001-01-01 0.7284 4000000 0.7290 4000000 NaN NaN NaN
2002-01-01 0.7284 5000000 0.7286 4000000 NaN NaN NaN
2003-01-01 0.7285 1000000 0.7286 4000000 NaN NaN NaN
2004-01-01 0.7285 4000000 0.7290 4000000 NaN NaN NaN
Note there is also pd.join, which can join DataFrames based on their indices,
and handle non-unique indices based on the how parameter. Rows with duplicate
index are not removed.
In [94]: df1.join(df2)
Out[94]:
price side timestamp bid bid_size offer
2000-01-01 0.7286 2 1451865675631331 0.7284 4000000 0.7285
2000-01-01 0.7286 2 1451865675631400 0.7284 4000000 0.7285
2001-01-01 0.7286 2 1451865675631861 0.7284 4000000 0.7290
2002-01-01 0.7286 2 1451865675631866 0.7284 5000000 0.7286
offer_size
2000-01-01 1000000
2000-01-01 1000000
2001-01-01 4000000
2002-01-01 4000000
In [95]: df1.join(df2, how='outer')
Out[95]:
price side timestamp bid bid_size offer offer_size
2000-01-01 0.7286 2 1.451866e+15 0.7284 4000000 0.7285 1000000
2000-01-01 0.7286 2 1.451866e+15 0.7284 4000000 0.7285 1000000
2001-01-01 0.7286 2 1.451866e+15 0.7284 4000000 0.7290 4000000
2002-01-01 0.7286 2 1.451866e+15 0.7284 5000000 0.7286 4000000
2003-01-01 NaN NaN NaN 0.7285 1000000 0.7286 4000000
2004-01-01 NaN NaN NaN 0.7285 4000000 0.7290 4000000
edited May 23 '17 at 12:34
Community♦
11
answered Jan 29 '16 at 15:26
unutbuunutbu
550k10111821239
edited May 23 '17 at 12:34
Community♦
11
edited May 23 '17 at 12:34
Community♦
11
edited May 23 '17 at 12:34
Community♦
11
11
answered Jan 29 '16 at 15:26
unutbuunutbu
550k10111821239
answered Jan 29 '16 at 15:26
unutbuunutbu
550k10111821239
answered Jan 29 '16 at 15:26
unutbuunutbu
550k10111821239
550k10111821239
@untubu - works a treat with join Thanks
– noidea
Jan 29 '16 at 17:30
add a comment |
@untubu - works a treat with join Thanks
– noidea
Jan 29 '16 at 17:30
@untubu - works a treat with join Thanks
– noidea
Jan 29 '16 at 17:30
@untubu - works a treat with join Thanks
– noidea
Jan 29 '16 at 17:30
add a comment |
10
You can mitigate this error without having to change your data or remove duplicates. Just create a new index with DataFrame.reset_index:
df = df.reset_index()
The old index is kept as a column in your dataframe, but if you don't need it you can do:
df = df.reset_index(drop=True)
Some prefer:
df.reset_index(inplace=True, drop=True)
answered Jul 12 '17 at 11:16
Nicholas MorleyNicholas Morley
1,316279
1
This should be marked asd the right answer, since it solves the problem without loosing information. Not always duplicate records are wrong on the datasets.
– Juanu Haedo
Nov 24 '17 at 19:29
add a comment |
10
You can mitigate this error without having to change your data or remove duplicates. Just create a new index with DataFrame.reset_index:
df = df.reset_index()
The old index is kept as a column in your dataframe, but if you don't need it you can do:
df = df.reset_index(drop=True)
Some prefer:
df.reset_index(inplace=True, drop=True)
answered Jul 12 '17 at 11:16
Nicholas MorleyNicholas Morley
1,316279
1
This should be marked asd the right answer, since it solves the problem without loosing information. Not always duplicate records are wrong on the datasets.
– Juanu Haedo
Nov 24 '17 at 19:29
add a comment |
10
10
10
You can mitigate this error without having to change your data or remove duplicates. Just create a new index with DataFrame.reset_index:
df = df.reset_index()
The old index is kept as a column in your dataframe, but if you don't need it you can do:
df = df.reset_index(drop=True)
Some prefer:
df.reset_index(inplace=True, drop=True)
answered Jul 12 '17 at 11:16
Nicholas MorleyNicholas Morley
1,316279
You can mitigate this error without having to change your data or remove duplicates. Just create a new index with DataFrame.reset_index:
df = df.reset_index()
The old index is kept as a column in your dataframe, but if you don't need it you can do:
df = df.reset_index(drop=True)
Some prefer:
df.reset_index(inplace=True, drop=True)
answered Jul 12 '17 at 11:16
Nicholas MorleyNicholas Morley
1,316279
edited Jul 12 '17 at 11:23
answered Jul 12 '17 at 11:16
Nicholas MorleyNicholas Morley
1,316279
answered Jul 12 '17 at 11:16
Nicholas MorleyNicholas Morley
1,316279
answered Jul 12 '17 at 11:16
Nicholas MorleyNicholas Morley
1,316279
1,316279
1
This should be marked asd the right answer, since it solves the problem without loosing information. Not always duplicate records are wrong on the datasets.
– Juanu Haedo
Nov 24 '17 at 19:29
add a comment |
1
This should be marked asd the right answer, since it solves the problem without loosing information. Not always duplicate records are wrong on the datasets.
– Juanu Haedo
Nov 24 '17 at 19:29
1
1
This should be marked asd the right answer, since it solves the problem without loosing information. Not always duplicate records are wrong on the datasets.
– Juanu Haedo
Nov 24 '17 at 19:29
This should be marked asd the right answer, since it solves the problem without loosing information. Not always duplicate records are wrong on the datasets.
– Juanu Haedo
Nov 24 '17 at 19:29
add a comment |
0
Another thing that might throw this type of errors is when you have a column with a unique value inside (entropy of 0).
In this case, you can either inject small Gaussian noise in that column or remove it completely.
answered Nov 21 '18 at 10:31
May Pilijay ElMay Pilijay El
113
add a comment |
0
Another thing that might throw this type of errors is when you have a column with a unique value inside (entropy of 0).
In this case, you can either inject small Gaussian noise in that column or remove it completely.
answered Nov 21 '18 at 10:31
May Pilijay ElMay Pilijay El
113
add a comment |
0
0
0
Another thing that might throw this type of errors is when you have a column with a unique value inside (entropy of 0).
In this case, you can either inject small Gaussian noise in that column or remove it completely.
answered Nov 21 '18 at 10:31
May Pilijay ElMay Pilijay El
113
Another thing that might throw this type of errors is when you have a column with a unique value inside (entropy of 0).
In this case, you can either inject small Gaussian noise in that column or remove it completely.
answered Nov 21 '18 at 10:31
May Pilijay ElMay Pilijay El
113
answered Nov 21 '18 at 10:31
May Pilijay ElMay Pilijay El
113
answered Nov 21 '18 at 10:31
May Pilijay ElMay Pilijay El
113
answered Nov 21 '18 at 10:31
May Pilijay ElMay Pilijay El
113
113
add a comment |
add a comment |
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what does
pd.concatdo?– gmoshkin
Jan 29 '16 at 12:05
@gmoshkin it places dataframes together as one dataframe - joined on the axis.
– noidea
Jan 29 '16 at 12:29
I have removed the column timestamp from both df1 and df2 and attempted to drop and NA with df1.dropna() and df2.dropna(); The problem persists....
– noidea
Jan 29 '16 at 12:49
I do not understand what neither
pdnordf1anddf2are. so I have no Idea neither what you are trying to do nor what goes wrong.– gmoshkin
Jan 29 '16 at 14:36
1
@gmoshkin, I am assuming that pd as an alias for pandas: import pandas as pd, and df1 and df2 are pandas DataFrame objects
– jugovich
Nov 16 '17 at 2:30