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| Pandas basic tutorial 4 - Python Programming |
Open
Anaconda Navigator
Launch
Sypder
If you have not seen the Pandas
PROGRAM
1
import pandas as pd
print ("\n MAX ROWS \n")
print
(pd.get_option("display.max_rows"))
print ("\n MAX COLUMNS \n")
print
(pd.get_option("display.max_columns"))
print ("\n SET OPTION MAX ROWS
\n")
pd.set_option("display.max_rows",70)
print
(pd.get_option("display.max_rows"))
print ("\n SET OPTION MAX COLUMNS
\n")
pd.set_option("display.max_columns",20)
print
(pd.get_option("display.max_columns"))
print ("\n RESET OPTION MAX ROWS
\n")
pd.reset_option("display.max_rows")
print
(pd.get_option("display.max_rows"))
print ("\n DESCRIBE OPTION MAX
ROWS \n")
pd.describe_option("display.max_rows")
print ("\n OPTION CONTEXT
MAX ROWS \n")
with
pd.option_context("display.max_rows",10):
print
(pd.get_option("display.max_rows"))
print(pd.get_option("display.max_rows"))
OUTPUT
MAX ROWS
60
MAX COLUMNS
20
SET OPTION MAX ROWS
70
SET OPTION MAX COLUMNS
20
RESET OPTION MAX ROWS
60
DESCRIBE OPTION MAX ROWS
display.max_rows : int
If max_rows is exceeded,
switch to truncate view. Depending on
`large_repr`, objects are
either centrally truncated or printed as
a summary view. 'None'
value means unlimited.
In case python/IPython is running in a terminal and `large_repr`
equals 'truncate' this
can be set to 0 and pandas will auto-detect
the height of the
terminal and print a truncated object which fits
the screen height. The
IPython notebook, IPython qtconsole, or
IDLE do not run in a
terminal and hence it is not possible to do
correct auto-detection.
[default: 60] [currently:
60]
OPTION CONTEXT MAX ROWS
10
10
PROGRAM
2
import pandas as pd
d = pd.DataFrame(np.random.randn(8, 4),
index =
['anita','babita','cavita','davita','evita','favita','gavita','hita'], columns
= ['A', 'B', 'C', 'D'])
print ('\n SELECT ALL ROWS OF A \n')
print (d.loc[:,'A'])
print ('\n SELECT ALL ROWS OF B \n')
print (d.loc[:,['D','B']])
print('\n')
print (d.loc[['anita','hita'],['A','C']])
print('\n')
print (d.loc['anita':'evita'])
print('\n')
print (d.loc['anita']>0)
OUTPUT
SELECT ALL ROWS OF A
anita -0.208319
babita -0.416149
cavita -1.026510
davita 0.857518
evita 2.033100
favita -1.154697
gavita -1.015738
hita -1.349647
Name: A, dtype: float64
SELECT ALL ROWS OF B
D B
anita 2.488755 -0.666601
babita -0.183947 0.406478
cavita -1.843386 -0.680845
davita -2.658799 0.516128
evita -0.199552 -0.694308
favita 1.343513 -1.924048
gavita 0.035830 1.738005
hita -0.345181 -0.211781
A C
anita -0.208319 1.653451
hita -1.349647 -0.238298
A B C D
anita -0.208319 -0.666601 1.653451 2.488755
babita -0.416149 0.406478 -1.432652 -0.183947
cavita -1.026510 -0.680845 1.366286 -1.843386
davita 0.857518 0.516128 -1.020205 -2.658799
evita 2.033100 -0.694308 -0.722837 -0.199552
A False
B False
C True
D True
Name: anita, dtype: bool
PROGRAM 3
import pandas as pd
d = pd.DataFrame(np.random.randn(8, 4), columns = ['A', 'B', 'C', 'D'])
print ('\nselect all rows for a specific column \n ')
print ('\n d.iloc[:4]\n')
print (d.iloc[:4])
print ('\n d.iloc[2:5, 3:4] \n')
print (d.iloc[2:5, 3:4])
print ('\n d.iloc[[1, 2, 4], [1, 3]]\n ')
print (d.iloc[[1, 2, 4], [1, 3]])
print ('\n d.iloc[1:4, :] \n')
print (d.iloc[1:4, :])
print ('\n d.iloc[:,1:3]\n ')
print (d.iloc[:,1:3])
print ('\n d.ix[:4] \n')
print (d.ix[:4]) #index slicing
print ('\n d.ix[:,\'A\'] \n')
print (d.ix[:,'A'])
print ('\n d[\'A\'] \n')
print (d['A'])
print ('\n d[[\'D','\B\']] \n')
print (d[['D','B']])
print ('\n d[2:2] \n')
print (d[2:2])
print (' \n d.B \n')
print (d.B)
OUTPUT
select all rows for a specific column
d.iloc[:4]
A B C D
0 1.265307 0.234860 -0.224934 -1.265009
1 -0.095622 1.455218 -0.216373 1.120572
2 -0.218187 0.160335 1.844968 0.903696
3 1.494101 -1.471412 0.059047 -0.539891
d.iloc[2:5, 3:4]
D
2 0.903696
3 -0.539891
4 0.762802
d.iloc[[1, 2, 4], [1, 3]]
B D
1 1.455218 1.120572
2 0.160335 0.903696
4 -0.178283 0.762802
d.iloc[1:4, :]
A B C D
1 -0.095622 1.455218 -0.216373 1.120572
2 -0.218187 0.160335 1.844968 0.903696
3 1.494101 -1.471412 0.059047 -0.539891
d.iloc[:,1:3]
B C
0 0.234860 -0.224934
1 1.455218 -0.216373
2 0.160335 1.844968
3 -1.471412 0.059047
4 -0.178283 -1.691390
5 1.176580 -1.511110
6 -0.513844 0.038202
7 -1.259789 -1.021303
d.ix[:4]
A B C D
0 1.265307 0.234860 -0.224934 -1.265009
1 -0.095622 1.455218 -0.216373 1.120572
2 -0.218187 0.160335 1.844968 0.903696
3 1.494101 -1.471412 0.059047 -0.539891
4 1.639339 -0.178283 -1.691390 0.762802
d.ix[:,'A']
0 1.265307
1 -0.095622
2 -0.218187
3 1.494101
4 1.639339
5 -0.649768
6 0.540650
7 -0.737735
Name: A, dtype: float64
d['A']
0 1.265307
1 -0.095622
2 -0.218187
3 1.494101
4 1.639339
5 -0.649768
6 0.540650
7 -0.737735
Name: A, dtype: float64
d[['D \B']]
D B
0 -1.265009 0.234860
1 1.120572 1.455218
2 0.903696 0.160335
3 -0.539891 -1.471412
4 0.762802 -0.178283
5 1.204925 1.176580
6 -1.238555 -0.513844
7 1.730290 -1.259789
d[2:2]
Empty DataFrame
Columns: [A, B, C, D]
Index: []
d.B
0 0.234860
1 1.455218
2 0.160335
3 -1.471412
4 -0.178283
5 1.176580
6 -0.513844
7 -1.259789
Name: B, dtype: float64