Pandas DataFrame concat vs append
Question:
I have a list of 4 pandas dataframes containing a day of tick data that I want to merge into a single data frame. I cannot understand the behavior of concat on my timestamps. See details below:
data
[<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 35228 entries, 2013-03-28 00:00:07.089000+02:00 to 2013-03-28 18:59:20.357000+02:00
Data columns:
Price 4040 non-null values
Volume 4040 non-null values
BidQty 35228 non-null values
BidPrice 35228 non-null values
AskPrice 35228 non-null values
AskQty 35228 non-null values
dtypes: float64(6),
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 33088 entries, 2013-04-01 00:03:17.047000+02:00 to 2013-04-01 18:59:58.175000+02:00
Data columns:
Price 3969 non-null values
Volume 3969 non-null values
BidQty 33088 non-null values
BidPrice 33088 non-null values
AskPrice 33088 non-null values
AskQty 33088 non-null values
dtypes: float64(6),
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 50740 entries, 2013-04-02 00:03:27.470000+02:00 to 2013-04-02 18:59:58.172000+02:00
Data columns:
Price 7326 non-null values
Volume 7326 non-null values
BidQty 50740 non-null values
BidPrice 50740 non-null values
AskPrice 50740 non-null values
AskQty 50740 non-null values
dtypes: float64(6),
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 60799 entries, 2013-04-03 00:03:06.994000+02:00 to 2013-04-03 18:59:58.180000+02:00
Data columns:
Price 8258 non-null values
Volume 8258 non-null values
BidQty 60799 non-null values
BidPrice 60799 non-null values
AskPrice 60799 non-null values
AskQty 60799 non-null values
dtypes: float64(6)]
Using append I get:
pd.DataFrame().append(data)
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 179855 entries, 2013-03-28 00:00:07.089000+02:00 to 2013-04-03 18:59:58.180000+02:00
Data columns:
AskPrice 179855 non-null values
AskQty 179855 non-null values
BidPrice 179855 non-null values
BidQty 179855 non-null values
Price 23593 non-null values
Volume 23593 non-null values
dtypes: float64(6)
Using concat I get:
pd.concat(data)
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 179855 entries, 2013-03-27 22:00:07.089000+02:00 to 2013-04-03 16:59:58.180000+02:00
Data columns:
Price 23593 non-null values
Volume 23593 non-null values
BidQty 179855 non-null values
BidPrice 179855 non-null values
AskPrice 179855 non-null values
AskQty 179855 non-null values
dtypes: float64(6)
Notice how the index changes when using concat. Why is that happening and how would I go about using concat to reproduce the results obtained using append? (Since concat seems so much faster; 24.6 ms per loop vs 3.02 s per loop)
Answers:
So what are you doing is with append and concat is almost equivalent. The difference is the empty DataFrame. For some reason this causes a big slowdown, not sure exactly why, will have to look at some point. Below is a recreation of basically what you did.
I almost always use concat (though in this case they are equivalent, except for the empty frame);
if you don’t use the empty frame they will be the same speed.
In [17]: df1 = pd.DataFrame(dict(A = range(10000)),index=pd.date_range('20130101',periods=10000,freq='s'))
In [18]: df1
Out[18]:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 10000 entries, 2013-01-01 00:00:00 to 2013-01-01 02:46:39
Freq: S
Data columns (total 1 columns):
A 10000 non-null values
dtypes: int64(1)
In [19]: df4 = pd.DataFrame()
The concat
In [20]: %timeit pd.concat([df1,df2,df3])
1000 loops, best of 3: 270 us per loop
This is equavalent of your append
In [21]: %timeit pd.concat([df4,df1,df2,df3])
10 loops, best of
3: 56.8 ms per loop
I have implemented a tiny benchmark (please find the code on Gist) to evaluate the pandas’ concat and append. I updated the code snippet and the results after the comment by ssk08 – thanks alot!
The benchmark ran on a Mac OS X 10.13 system with Python 3.6.2 and pandas 0.20.3.
+--------+---------------------------------+---------------------------------+
| | ignore_index=False | ignore_index=True |
+--------+---------------------------------+---------------------------------+
| size | append | concat | append/concat | append | concat | append/concat |
+--------+--------+--------+---------------+--------+--------+---------------+
| small | 0.4635 | 0.4891 | 94.77 % | 0.4056 | 0.3314 | 122.39 % |
+--------+--------+--------+---------------+--------+--------+---------------+
| medium | 0.5532 | 0.6617 | 83.60 % | 0.3605 | 0.3521 | 102.37 % |
+--------+--------+--------+---------------+--------+--------+---------------+
| large | 0.9558 | 0.9442 | 101.22 % | 0.6670 | 0.6749 | 98.84 % |
+--------+--------+--------+---------------+--------+--------+---------------+
Using ignore_index=False append is slightly faster, with ignore_index=True concat is slightly faster.
tl;dr
No significant difference between concat and append.
Pandas concat vs append vs join vs merge
-
Concat gives the flexibility to join based on the axis( all rows or all columns)
-
Append is the specific case(axis=0, join=’outer’) of concat (being deprecated use concat)
-
Join is based on the indexes (set by set_index) on how variable =[‘left’,’right’,’inner’,’couter’]
-
Merge is based on any particular column each of the two dataframes, this columns are variables on like ‘left_on’, ‘right_on’, ‘on’
One more thing you have to keep in mind that the APPEND() method in Pandas doesn’t modify the original object. Instead it creates a new one with combined data. Because of involving creation and data buffer, its performance is not well. You’d better use CONCAT() function when doing multi-APPEND operations.
I have a list of 4 pandas dataframes containing a day of tick data that I want to merge into a single data frame. I cannot understand the behavior of concat on my timestamps. See details below:
data
[<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 35228 entries, 2013-03-28 00:00:07.089000+02:00 to 2013-03-28 18:59:20.357000+02:00
Data columns:
Price 4040 non-null values
Volume 4040 non-null values
BidQty 35228 non-null values
BidPrice 35228 non-null values
AskPrice 35228 non-null values
AskQty 35228 non-null values
dtypes: float64(6),
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 33088 entries, 2013-04-01 00:03:17.047000+02:00 to 2013-04-01 18:59:58.175000+02:00
Data columns:
Price 3969 non-null values
Volume 3969 non-null values
BidQty 33088 non-null values
BidPrice 33088 non-null values
AskPrice 33088 non-null values
AskQty 33088 non-null values
dtypes: float64(6),
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 50740 entries, 2013-04-02 00:03:27.470000+02:00 to 2013-04-02 18:59:58.172000+02:00
Data columns:
Price 7326 non-null values
Volume 7326 non-null values
BidQty 50740 non-null values
BidPrice 50740 non-null values
AskPrice 50740 non-null values
AskQty 50740 non-null values
dtypes: float64(6),
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 60799 entries, 2013-04-03 00:03:06.994000+02:00 to 2013-04-03 18:59:58.180000+02:00
Data columns:
Price 8258 non-null values
Volume 8258 non-null values
BidQty 60799 non-null values
BidPrice 60799 non-null values
AskPrice 60799 non-null values
AskQty 60799 non-null values
dtypes: float64(6)]
Using append I get:
pd.DataFrame().append(data)
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 179855 entries, 2013-03-28 00:00:07.089000+02:00 to 2013-04-03 18:59:58.180000+02:00
Data columns:
AskPrice 179855 non-null values
AskQty 179855 non-null values
BidPrice 179855 non-null values
BidQty 179855 non-null values
Price 23593 non-null values
Volume 23593 non-null values
dtypes: float64(6)
Using concat I get:
pd.concat(data)
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 179855 entries, 2013-03-27 22:00:07.089000+02:00 to 2013-04-03 16:59:58.180000+02:00
Data columns:
Price 23593 non-null values
Volume 23593 non-null values
BidQty 179855 non-null values
BidPrice 179855 non-null values
AskPrice 179855 non-null values
AskQty 179855 non-null values
dtypes: float64(6)
Notice how the index changes when using concat. Why is that happening and how would I go about using concat to reproduce the results obtained using append? (Since concat seems so much faster; 24.6 ms per loop vs 3.02 s per loop)
So what are you doing is with append and concat is almost equivalent. The difference is the empty DataFrame. For some reason this causes a big slowdown, not sure exactly why, will have to look at some point. Below is a recreation of basically what you did.
I almost always use concat (though in this case they are equivalent, except for the empty frame);
if you don’t use the empty frame they will be the same speed.
In [17]: df1 = pd.DataFrame(dict(A = range(10000)),index=pd.date_range('20130101',periods=10000,freq='s'))
In [18]: df1
Out[18]:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 10000 entries, 2013-01-01 00:00:00 to 2013-01-01 02:46:39
Freq: S
Data columns (total 1 columns):
A 10000 non-null values
dtypes: int64(1)
In [19]: df4 = pd.DataFrame()
The concat
In [20]: %timeit pd.concat([df1,df2,df3])
1000 loops, best of 3: 270 us per loop
This is equavalent of your append
In [21]: %timeit pd.concat([df4,df1,df2,df3])
10 loops, best of
3: 56.8 ms per loop
I have implemented a tiny benchmark (please find the code on Gist) to evaluate the pandas’ concat and append. I updated the code snippet and the results after the comment by ssk08 – thanks alot!
The benchmark ran on a Mac OS X 10.13 system with Python 3.6.2 and pandas 0.20.3.
+--------+---------------------------------+---------------------------------+ | | ignore_index=False | ignore_index=True | +--------+---------------------------------+---------------------------------+ | size | append | concat | append/concat | append | concat | append/concat | +--------+--------+--------+---------------+--------+--------+---------------+ | small | 0.4635 | 0.4891 | 94.77 % | 0.4056 | 0.3314 | 122.39 % | +--------+--------+--------+---------------+--------+--------+---------------+ | medium | 0.5532 | 0.6617 | 83.60 % | 0.3605 | 0.3521 | 102.37 % | +--------+--------+--------+---------------+--------+--------+---------------+ | large | 0.9558 | 0.9442 | 101.22 % | 0.6670 | 0.6749 | 98.84 % | +--------+--------+--------+---------------+--------+--------+---------------+
Using ignore_index=False append is slightly faster, with ignore_index=True concat is slightly faster.
tl;dr
No significant difference between concat and append.
Pandas concat vs append vs join vs merge
-
Concat gives the flexibility to join based on the axis( all rows or all columns)
-
Append is the specific case(axis=0, join=’outer’) of concat (being deprecated use concat)
-
Join is based on the indexes (set by set_index) on how variable =[‘left’,’right’,’inner’,’couter’]
-
Merge is based on any particular column each of the two dataframes, this columns are variables on like ‘left_on’, ‘right_on’, ‘on’
One more thing you have to keep in mind that the APPEND() method in Pandas doesn’t modify the original object. Instead it creates a new one with combined data. Because of involving creation and data buffer, its performance is not well. You’d better use CONCAT() function when doing multi-APPEND operations.