Probelem with Pandas dataframe merge
0
I tried to us the pandas merge function but I got an keyerror message. The keys have the same type and the same name, what could be the problem:
My code is here:
print(data_EURUSD.head())
print(data_XAUUSD.head())
print(data_EURUSD.index)
print(data_XAUUSD.index)
data=pd.merge(data_EURUSD, data_XAUUSD, on='date', how='left')
print(data)
The result:
askclose
date
2017-05-19 21:00:00 1.12090
2017-05-21 21:00:00 1.11999
2017-05-22 21:00:00 1.12402
2017-05-23 21:00:00 1.11840
2017-05-24 21:00:00 1.12195
askclose
date
2017-01-20 22:00:00 1209.47
2017-01-23 22:00:00 1218.46
2017-01-24 22:00:00 1209.41
2017-01-25 22:00:00 1201.18
2017-01-26 22:00:00 1189.04
DatetimeIndex(['2017-05-19 21:00:00', '2017-05-21 21:00:00',
'2018-12-26 22:00:00', '2018-12-27 22:00:00'],
dtype='datetime64[ns]', name='date', length=500, freq=None)
DatetimeIndex(['2017-01-20 22:00:00', '2017-01-23 22:00:00',
'2018-12-26 22:00:00', '2018-12-27 22:00:00'],
dtype='datetime64[ns]', name='date', length=500, freq=None)
....
KeyError: 'date'
pandas dataframe merge
asked Dec 28 '18 at 16:27
Roland SzarkaRoland Szarka
211
add a comment |
0
I tried to us the pandas merge function but I got an keyerror message. The keys have the same type and the same name, what could be the problem:
My code is here:
print(data_EURUSD.head())
print(data_XAUUSD.head())
print(data_EURUSD.index)
print(data_XAUUSD.index)
data=pd.merge(data_EURUSD, data_XAUUSD, on='date', how='left')
print(data)
The result:
askclose
date
2017-05-19 21:00:00 1.12090
2017-05-21 21:00:00 1.11999
2017-05-22 21:00:00 1.12402
2017-05-23 21:00:00 1.11840
2017-05-24 21:00:00 1.12195
askclose
date
2017-01-20 22:00:00 1209.47
2017-01-23 22:00:00 1218.46
2017-01-24 22:00:00 1209.41
2017-01-25 22:00:00 1201.18
2017-01-26 22:00:00 1189.04
DatetimeIndex(['2017-05-19 21:00:00', '2017-05-21 21:00:00',
'2018-12-26 22:00:00', '2018-12-27 22:00:00'],
dtype='datetime64[ns]', name='date', length=500, freq=None)
DatetimeIndex(['2017-01-20 22:00:00', '2017-01-23 22:00:00',
'2018-12-26 22:00:00', '2018-12-27 22:00:00'],
dtype='datetime64[ns]', name='date', length=500, freq=None)
....
KeyError: 'date'
pandas dataframe merge
asked Dec 28 '18 at 16:27
Roland SzarkaRoland Szarka
211
Happy to help, and welcome to Stack Overflow. If this answer or any other one solved your issue, please mark it as accepted.
– Lucas H
Dec 28 '18 at 16:44
add a comment |
0
0
0
I tried to us the pandas merge function but I got an keyerror message. The keys have the same type and the same name, what could be the problem:
My code is here:
print(data_EURUSD.head())
print(data_XAUUSD.head())
print(data_EURUSD.index)
print(data_XAUUSD.index)
data=pd.merge(data_EURUSD, data_XAUUSD, on='date', how='left')
print(data)
The result:
askclose
date
2017-05-19 21:00:00 1.12090
2017-05-21 21:00:00 1.11999
2017-05-22 21:00:00 1.12402
2017-05-23 21:00:00 1.11840
2017-05-24 21:00:00 1.12195
askclose
date
2017-01-20 22:00:00 1209.47
2017-01-23 22:00:00 1218.46
2017-01-24 22:00:00 1209.41
2017-01-25 22:00:00 1201.18
2017-01-26 22:00:00 1189.04
DatetimeIndex(['2017-05-19 21:00:00', '2017-05-21 21:00:00',
'2018-12-26 22:00:00', '2018-12-27 22:00:00'],
dtype='datetime64[ns]', name='date', length=500, freq=None)
DatetimeIndex(['2017-01-20 22:00:00', '2017-01-23 22:00:00',
'2018-12-26 22:00:00', '2018-12-27 22:00:00'],
dtype='datetime64[ns]', name='date', length=500, freq=None)
....
KeyError: 'date'
pandas dataframe merge
asked Dec 28 '18 at 16:27
Roland SzarkaRoland Szarka
211
I tried to us the pandas merge function but I got an keyerror message. The keys have the same type and the same name, what could be the problem:
My code is here:
print(data_EURUSD.head())
print(data_XAUUSD.head())
print(data_EURUSD.index)
print(data_XAUUSD.index)
data=pd.merge(data_EURUSD, data_XAUUSD, on='date', how='left')
print(data)
The result:
askclose
date
2017-05-19 21:00:00 1.12090
2017-05-21 21:00:00 1.11999
2017-05-22 21:00:00 1.12402
2017-05-23 21:00:00 1.11840
2017-05-24 21:00:00 1.12195
askclose
date
2017-01-20 22:00:00 1209.47
2017-01-23 22:00:00 1218.46
2017-01-24 22:00:00 1209.41
2017-01-25 22:00:00 1201.18
2017-01-26 22:00:00 1189.04
DatetimeIndex(['2017-05-19 21:00:00', '2017-05-21 21:00:00',
'2018-12-26 22:00:00', '2018-12-27 22:00:00'],
dtype='datetime64[ns]', name='date', length=500, freq=None)
DatetimeIndex(['2017-01-20 22:00:00', '2017-01-23 22:00:00',
'2018-12-26 22:00:00', '2018-12-27 22:00:00'],
dtype='datetime64[ns]', name='date', length=500, freq=None)
....
KeyError: 'date'
pandas dataframe merge
pandas dataframe merge
asked Dec 28 '18 at 16:27
Roland SzarkaRoland Szarka
211
asked Dec 28 '18 at 16:27
Roland SzarkaRoland Szarka
211
asked Dec 28 '18 at 16:27
Roland SzarkaRoland Szarka
211
asked Dec 28 '18 at 16:27
Roland SzarkaRoland Szarka
211
asked Dec 28 '18 at 16:27
Roland SzarkaRoland Szarka
211
211
Happy to help, and welcome to Stack Overflow. If this answer or any other one solved your issue, please mark it as accepted.
– Lucas H
Dec 28 '18 at 16:44
add a comment |
Happy to help, and welcome to Stack Overflow. If this answer or any other one solved your issue, please mark it as accepted.
– Lucas H
Dec 28 '18 at 16:44
Happy to help, and welcome to Stack Overflow. If this answer or any other one solved your issue, please mark it as accepted.
– Lucas H
Dec 28 '18 at 16:44
Happy to help, and welcome to Stack Overflow. If this answer or any other one solved your issue, please mark it as accepted.
– Lucas H
Dec 28 '18 at 16:44
add a comment |
1 Answer
1
active
oldest
votes
1
This error is because you have a datetime index, 'date' is not a column it is a index value.
You should do:
pd.merge(data_EURUSD, data_XAUUSD, how='left',right_index=True,left_index=True)
In these situations I prefer to use pd.df.join() which already seeks to join based on shared indices. The code would be:
data = data_EURUSD.join(data_XAUUSD,how='left')
answered Dec 28 '18 at 16:32
Lucas HLucas H
864
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
1
This error is because you have a datetime index, 'date' is not a column it is a index value.
You should do:
pd.merge(data_EURUSD, data_XAUUSD, how='left',right_index=True,left_index=True)
In these situations I prefer to use pd.df.join() which already seeks to join based on shared indices. The code would be:
data = data_EURUSD.join(data_XAUUSD,how='left')
answered Dec 28 '18 at 16:32
Lucas HLucas H
864
add a comment |
1
This error is because you have a datetime index, 'date' is not a column it is a index value.
You should do:
pd.merge(data_EURUSD, data_XAUUSD, how='left',right_index=True,left_index=True)
In these situations I prefer to use pd.df.join() which already seeks to join based on shared indices. The code would be:
data = data_EURUSD.join(data_XAUUSD,how='left')
answered Dec 28 '18 at 16:32
Lucas HLucas H
864
add a comment |
1
1
1
This error is because you have a datetime index, 'date' is not a column it is a index value.
You should do:
pd.merge(data_EURUSD, data_XAUUSD, how='left',right_index=True,left_index=True)
In these situations I prefer to use pd.df.join() which already seeks to join based on shared indices. The code would be:
data = data_EURUSD.join(data_XAUUSD,how='left')
answered Dec 28 '18 at 16:32
Lucas HLucas H
864
This error is because you have a datetime index, 'date' is not a column it is a index value.
You should do:
pd.merge(data_EURUSD, data_XAUUSD, how='left',right_index=True,left_index=True)
In these situations I prefer to use pd.df.join() which already seeks to join based on shared indices. The code would be:
data = data_EURUSD.join(data_XAUUSD,how='left')
answered Dec 28 '18 at 16:32
Lucas HLucas H
864
answered Dec 28 '18 at 16:32
Lucas HLucas H
864
answered Dec 28 '18 at 16:32
Lucas HLucas H
864
answered Dec 28 '18 at 16:32
Lucas HLucas H
864
864
add a comment |
add a comment |
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Happy to help, and welcome to Stack Overflow. If this answer or any other one solved your issue, please mark it as accepted.
– Lucas H
Dec 28 '18 at 16:44