Pandas Quick Start

This is a Pandas QuickStart tutorial that is primarily geared toward new users. This is mainly for those who like "Chanping" readers, interested readers can use the other tutorial articles to step by step more complex application knowledge.

First, let's say you've installed Anaconda, now start Anaconda and start learning the examples in this tutorial. The working interface is shown below-

Test the working environment for installation of Pandas, import the related package as follows:

Import pandas as PD
import numpy as NP
import Matplotlib.pyplot as Plt
print ("Hello, Pandas")

Python

Then execute it to see if there is a problem, if normal should see the following results in the terminal output area-

Object Creation

Create a series by passing a list of values so that pandas creates a default integer index :

Import pandas as PD
import numpy as np

s = PD. Series ([1,3,5,np.nan,6,8])

print (s)

Python

After execution the output is as follows-

Runfile (' c:/users/administrator/.spyder-py3/temp.py ', wdir= ' c:/users/administrator/.spyder-py3 ')
0    1.0
1    3.0
2    5.0
3    NaN
4    6.0
5    8.0
Dtype:float64

Shell

Create dataframe using a datetime index and a tagged column by passing the NumPy array:

Import pandas as PD
import numpy as np

dates = pd.date_range (' 20170101 ', periods=7)
print ( Dates)

print ("---" *16)
= PD.  Dataframe(np.random.randn (7,4), index=dates, columns=list (' ABCD '))
print (DF)

Python

After execution the output is as follows-

Runfile (' c:/users/administrator/.spyder-py3/temp.py ', wdir= ' c:/users/administrator/.spyder-py3 ')
Datetimeindex ([' 2017-01-01 ', ' 2017-01-02 ', ' 2017-01-03 ', ' 2017-01-04 ',
               ' 2017-01-05 ', ' 2017-01-06 ', ' 2017-01-07 ') ],
              dtype= ' datetime64[ns] ', freq= ' d ')
--------------------------------
                   A         B         C         D
2017-01-01-0.732038  0.329773-0.156383  0.270800
2017-01-02  0.750144  0.722037-0.849848- 1.105319
2017-01-03-0.786664-0.204211  1.246395  0.292975
2017-01-04-1.108991  2.228375  0.079700-1.738507
2017-01-05  0.348526-0.960212  0.190978-2.223966
2017-01-06-0.579689 -1.355910  0.095982  1.233833
2017-01-07  1.086872  0.664982  0.377787  1.012772

Shell

Create a dataframe by passing a dictionary that can be converted to a similar series of objects. Refer to the following sample code-

Import pandas as PD
import numpy as np

DF2 = PD. Dataframe ({' A ': 1.,
                     ' B ': PD. Timestamp (' 20170102 '),
                     ' C ': PD. Series (1,index=list (range (4)), dtype= ' float32 '),
                     ' D ': Np.array ([3] * 4,dtype= ' int32 '),
                     ' E ': PD. Categorical (["Test", "Train", "Test", "Train"]),
                     ' F ': ' foo '}
'
print (DF2)

Python

After executing the sample code above, the output is as follows-

Runfile (' c:/users/administrator/.spyder-py3/temp.py ', wdir= ' c:/users/administrator/.spyder-py3 ')
     A          B    C  D      E    F
0  1.0 2017-01-02  1.0  3   Test  foo
1  1.0 2017-01-02  1.0  3  train  foo
2  1.0 2017-01-02  1.0  3   Test  foo
3  1.0 2017-01-02  1.0  3  train  foo

Shell

With the specified Dtypes, refer to the following sample code-

Runfile (' c:/users/administrator/.spyder-py3/temp.py ', wdir= ' c:/users/administrator/.spyder-py3 ')
A           Float64
B    datetime64[ns]
C           float32
D             int32
E          category
F            object
Dtype:object

Python

After executing the sample code above, the output is as follows-

Runfile (' c:/users/administrator/.spyder-py3/temp.py ', wdir= ' c:/users/administrator/.spyder-py3 ')
A           Float64
B    datetime64[ns]
C           float32
D             int32
E          category
F            Object
Dtype:object

Shell

If you use Ipython, the option to automatically enable column names (as well as public properties) is complete . The following is a subset of the properties that will be completed:

in [[]: Df2.<tab>
df2. A                  df2.bool
df2.abs                df2.boxplot
df2.add                df2. C
df2.add_prefix         df2.clip
df2.add_suffix         df2.clip_lower
df2.align              df2.clip_upper
df2.all                df2.columns
df2.any                df2.combine             df2.append
Df2.combine_first Df2.apply              df2.compound
df2.applymap           df2.consolidate
df2. D

Python

As you can see, the columns A,b,c and D auto tag are complete. E is the same. The remaining attributes are truncated for simplicity. View Data

View the data rows at the top and bottom of the frame. Refer to the following sample code-

Import pandas as PD
import NumPy as NP