Import pandas as pd Import numpy as np Data =np.array([55,68,98,26,37]) s1=pd.Seires (data,index=[‘a’, ‘b’,’c’, ‘d’, ‘e’]) s2=s1.reindex ([‘b’,’d’,’a’,’c’,’e’]) s3=s1.reindex([‘f’, ‘g’, ‘c’, ‘b’, ‘a’]) print(s1) print(s2) print(s3) find output
Answers
Answer:
Explanation:This is a short introduction to pandas, geared mainly for new users. You can see more complex recipes in the Cookbook.
Customarily, we import as follows:
In [1]: import numpy as np
In [2]: import pandas as pd
Object creation
See the Data Structure Intro section.
Creating a Series by passing a list of values, letting pandas create a default integer index:
In [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8])
In [4]: s
Out[4]:
0 1.0
1 3.0
2 5.0
3 NaN
4 6.0
5 8.0
dtype: float64
Creating a DataFrame by passing a NumPy array, with a datetime index and labeled columns:
In [5]: dates = pd.date_range('20130101', periods=6)
In [6]: dates
Out[6]:
DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
'2013-01-05', '2013-01-06'],
dtype='datetime64[ns]', freq='D')
In [7]: df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))
In [8]: df
Out[8]:
A B C D
2013-01-01 0.469112 -0.282863 -1.509059 -1.135632
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
2013-01-05 -0.424972 0.567020 0.276232 -1.087401
2013-01-06 -0.673690 0.113648 -1.478427 0.524988