35.pandas operations

### Create a dataframe

head()
unique()
count()
len(  )
nunique()
value_counts()
sum()
applying the function
apply lamba functions
sort_values(  )
isnull()
pivot_table()

import numpy as np
import pandas as pd

df = pd.DataFrame({'col1':[1,2,3,4],
                   'col2':[444,555,666,444],
                   'col3':['abc','def','ghi','xyz']})

print(df)

   col1  col2 col3
0     1   444  abc
1     2   555  def
2     3   666  ghi
3     4   444  xyz

df.head()  # first 5 rows

	col1	col2	col3
0	1	444	abc
1	2	555	def
2	3	666	ghi
3	4	444	xyz

df.head(3) 

	col1	col2	col3
0	1	444	abc
1	2	555	def
2	3	666	ghi

df.tail(2)

	col1	col2	col3
2	3	666	ghi
3	4	444	xyz

df

	col1	col2	col3
0	1	444	abc
1	2	555	def
2	3	666	ghi
3	4	444	xyz

df['col2'].unique()

array([444, 555, 666])

df['col2'].count()

4

df.count()

col1    4
col2    4
col3    4
dtype: int64

df['col1'].nunique()

4

df['col2'].nunique()

3

df['col2'].value_counts()

444    2
555    1
666    1
Name: col2, dtype: int64

len(df)

4

len(df['col1'])

4

df

	col1	col2	col3
0	1	444	abc
1	2	555	def
2	3	666	ghi
3	4	444	xyz

df['col2'].sort_values()

0    444
3    444
1    555
2    666
Name: col2, dtype: int64

df.isnull()

	col1	col2	col3
0	False	False	False
1	False	False	False
2	False	False	False
3	False	False	False

df['col1'].sum()

10

df['col1'].mean()

2.5

def times2(x):
    return x*2

df['col1'].apply(times2)

0    2
1    4
2    6
3    8
Name: col1, dtype: int64

df['col1'].apply(lambda x:x*2)

0    2
1    4
2    6
3    8
Name: col1, dtype: int64

df.apply(len)

col1    4
col2    4
col3    4
dtype: int64

data = {'A':['foo','foo','foo','bar','bar','bar'],
        'B':['one','one','two','two','one','one'],
        'C':['x','y','x','y','x','y'],
        'D':[1,3,2,5,4,1]}

df = pd.DataFrame(data)

df

	A	B	C	D
0	foo	one	x	1
1	foo	one	y	3
2	foo	two	x	2
3	bar	two	y	5
4	bar	one	x	4
5	bar	one	y	1

df.pivot_table('D', index=['A','B'], columns=['C'])

	C	x	y
A	B
bar	one	4.0	1.0
	two	NaN	5.0
foo	one	1.0	3.0
	two	2.0	NaN