Pandas provides various facilities for easily combining together Series, DataFrame, and Panel objects.
pd.concat(objs,axis=0,join='outer',join_axes=None,
ignore_index=False)
objs− This is a sequence or mapping of Series, DataFrame, or Panel objects.axis− {0, 1, ...}, default 0. This is the axis to concatenate along.join− {‘inner’, ‘outer’}, default ‘outer’. How to handle indexes on other axis(es). Outer for union and inner for intersection.ignore_indexboolean, default False. If True, do not use the index values on the concatenation axis. The resulting axis will be labeled 0, ..., n - 1.join_axes− This is the list of Index objects. Specific indexes to use for the other (n-1) axes instead of performing inner/outer set logic.
The concat function does all of the heavy lifting of performing concatenation operations along an axis. Let us create different objects and do concatenation.
import pandas as pd
one = pd.DataFrame({
'Name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],
'subject_id':['sub1','sub2','sub4','sub6','sub5'],
'Marks_scored':[98,90,87,69,78]},
index=[1,2,3,4,5])
two = pd.DataFrame({
'Name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
'subject_id':['sub2','sub4','sub3','sub6','sub5'],
'Marks_scored':[89,80,79,97,88]},
index=[1,2,3,4,5])
print(pd.concat([one,two])) Marks_scored Name subject_id
1 98 Alex sub1
2 90 Amy sub2
3 87 Allen sub4
4 69 Alice sub6
5 78 Ayoung sub5
1 89 Billy sub2
2 80 Brian sub4
3 79 Bran sub3
4 97 Bryce sub6
5 88 Betty sub5
Suppose we wanted to associate specific keys with each of the pieces of the chopped up DataFrame. We can do this by using the keys argument −
import pandas as pd
one = pd.DataFrame({
'Name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],
'subject_id':['sub1','sub2','sub4','sub6','sub5'],
'Marks_scored':[98,90,87,69,78]},
index=[1,2,3,4,5])
two = pd.DataFrame({
'Name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
'subject_id':['sub2','sub4','sub3','sub6','sub5'],
'Marks_scored':[89,80,79,97,88]},
index=[1,2,3,4,5])
print(pd.concat([one,two],keys=['x','y'])) Marks_scored Name subject_id
x 1 98 Alex sub1
2 90 Amy sub2
3 87 Allen sub4
4 69 Alice sub6
5 78 Ayoung sub5
y 1 89 Billy sub2
2 80 Brian sub4
3 79 Bran sub3
4 97 Bryce sub6
5 88 Betty sub5
The index of the resultant is duplicated; each index is repeated. If the resultant object has to follow its own indexing, set ignore_index to True.
import pandas as pd
one = pd.DataFrame({
'Name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],
'subject_id':['sub1','sub2','sub4','sub6','sub5'],
'Marks_scored':[98,90,87,69,78]},
index=[1,2,3,4,5])
two = pd.DataFrame({
'Name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
'subject_id':['sub2','sub4','sub3','sub6','sub5'],
'Marks_scored':[89,80,79,97,88]},
index=[1,2,3,4,5])
print(pd.concat([one,two],keys=['x','y'],ignore_index=True)) Marks_scored Name subject_id
0 98 Alex sub1
1 90 Amy sub2
2 87 Allen sub4
3 69 Alice sub6
4 78 Ayoung sub5
5 89 Billy sub2
6 80 Brian sub4
7 79 Bran sub3
8 97 Bryce sub6
9 88 Betty sub5
Observe, the index changes completely and the Keys are also overridden. If two objects need to be added along axis=1, then the new columns will be appended.
import pandas as pd
one = pd.DataFrame({
'Name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],
'subject_id':['sub1','sub2','sub4','sub6','sub5'],
'Marks_scored':[98,90,87,69,78]},
index=[1,2,3,4,5])
two = pd.DataFrame({
'Name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
'subject_id':['sub2','sub4','sub3','sub6','sub5'],
'Marks_scored':[89,80,79,97,88]},
index=[1,2,3,4,5])
print(pd.concat([one,two],axis=1)) Marks_scored Name subject_id Marks_scored Name subject_id
1 98 Alex sub1 89 Billy sub2
2 90 Amy sub2 80 Brian sub4
3 87 Allen sub4 79 Bran sub3
4 69 Alice sub6 97 Bryce sub6
5 78 Ayoung sub5 88 Betty sub5
A useful shortcut to concat are the append instance methods on Series and DataFrame. These methods actually predated concat. They concatenate along axis=0, namely the index −
import pandas as pd
one = pd.DataFrame({
'Name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],
'subject_id':['sub1','sub2','sub4','sub6','sub5'],
'Marks_scored':[98,90,87,69,78]},
index=[1,2,3,4,5])
two = pd.DataFrame({
'Name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
'subject_id':['sub2','sub4','sub3','sub6','sub5'],
'Marks_scored':[89,80,79,97,88]},
index=[1,2,3,4,5])
print(one.append(two)) Marks_scored Name subject_id
1 98 Alex sub1
2 90 Amy sub2
3 87 Allen sub4
4 69 Alice sub6
5 78 Ayoung sub5
1 89 Billy sub2
2 80 Brian sub4
3 79 Bran sub3
4 97 Bryce sub6
5 88 Betty sub5
The append function can take multiple objects as well −
import pandas as pd
one = pd.DataFrame({
'Name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],
'subject_id':['sub1','sub2','sub4','sub6','sub5'],
'Marks_scored':[98,90,87,69,78]},
index=[1,2,3,4,5])
two = pd.DataFrame({
'Name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
'subject_id':['sub2','sub4','sub3','sub6','sub5'],
'Marks_scored':[89,80,79,97,88]},
index=[1,2,3,4,5])
print(one.append([two,one,two])) Marks_scored Name subject_id
1 98 Alex sub1
2 90 Amy sub2
3 87 Allen sub4
4 69 Alice sub6
5 78 Ayoung sub5
1 89 Billy sub2
2 80 Brian sub4
3 79 Bran sub3
4 97 Bryce sub6
5 88 Betty sub5
1 98 Alex sub1
2 90 Amy sub2
3 87 Allen sub4
4 69 Alice sub6
5 78 Ayoung sub5
1 89 Billy sub2
2 80 Brian sub4
3 79 Bran sub3
4 97 Bryce sub6
5 88 Betty sub5
Pandas provide a robust tool for working time with Time series data, especially in the financial sector. While working with time series data, we frequently come across the following −
Convert the time series to different frequencies Pandas provides a relatively compact and self-contained set of tools for performing the above tasks.
Get Current Time
datetime.now() gives you the current date and time.
import pandas as pd
print pd.datetime.now()
Its output is as follows −
2017-05-11 06:10:13.393147Create a TimeStamp
Time-stamped data is the most basic type of timeseries data that associates values with points in time. For pandas objects, it means using the points in time. Let’s take an example −import pandas as pd
print pd.Timestamp('2017-03-01')It is also possible to convert integer or float epoch times. The default unit for these is nanoseconds (since these are how Timestamps are stored). However, often epochs are stored in another unit which can be specified. Let’s take another exampleimport pandas as pd
print pd.Timestamp(1587687255,unit='s')### Create a Range of Time
import pandas as pd
print pd.date_range("11:00", "13:30", freq="30min").time
Its output is as follows −
[datetime.time(11, 0) datetime.time(11, 30) datetime.time(12, 0)
datetime.time(12, 30) datetime.time(13, 0) datetime.time(13, 30)]Change the Frequency of Time
import pandas as pd
print pd.date_range("11:00", "13:30", freq="H").time
Its output is as follows −
[datetime.time(11, 0) datetime.time(12, 0) datetime.time(13, 0)]Converting to Timestamps
To convert a Series or list-like object of date-like objects, for example strings, epochs, or a mixture, you can use the to_datetime function. When passed, this returns a Series (with the same index), while a list-like is converted to a DatetimeIndex. Take a look at the following example −import pandas as pd
print pd.to_datetime(pd.Series(['Jul 31, 2009','2010-01-10', None]))
Its output is as follows −
0 2009-07-31
1 2010-01-10
2 NaT
dtype: datetime64[ns]
NaT means Not a Time (equivalent to NaN)Let’s take another example.import pandas as pd
print pd.to_datetime(['2005/11/23', '2010.12.31', None])
Its output is as follows −
DatetimeIndex(['2005-11-23', '2010-12-31', 'NaT'], dtype='datetime64[ns]', freq=None)