python commands pandas

Series

You can convert a list,numpy array, or dictionary to a Series:

labels = ['a','b','c']
my_list = [10,20,30]
arr = np.array([10,20,30])

d = {'a':10,'b':20,'c':30}

pd.Series(data=my_list)

pd.Series(data=my_list,index=labels)

pd.Series(d)

#series can even hold function, though its very unlikely that we will use it


Using an index

ser1 = pd.Series([1,2,3,4],index = ['USA', 'Germany','USSR', 'Japan'])         

USA        1
Germany    2
USSR       3
Japan      4
dtype: int64

ser2 = pd.Series([1,2,5,4],index = ['USA', 'Germany','Italy', 'Japan']) 

ser1 + ser2

Germany    4.0
Italy      NaN
Japan      8.0
USA        2.0
USSR       NaN




Data Frames
We can think of a DataFrame as a bunch of Series objects 




df = pd.DataFrame(randn(5,4),index='A B C D E'.split(),columns='W X Y Z'.split())











W
X
Y
Z

A
2.706850
0.628133
0.907969
0.503826
B
0.651118
-0.319318
-0.848077
0.605965
C
-2.018168
0.740122
0.528813
-0.589001
D
0.188695
-0.758872
-0.933237
0.955057
E
0.190794
1.978757
2.605967
0.683509
















df['new'] = df['W'] + df['Y']





W
X
Y
Z
new

A
2.706850
0.628133
0.907969
0.503826
3.614819
B
0.651118
-0.319318
-0.848077
0.605965
-0.196959
C
-2.018168
0.740122
0.528813
-0.589001
-1.489355
D
0.188695
-0.758872
-0.933237
0.955057
-0.744542
E
0.190794
1.978757
2.605967
0.683509
2.796762






df>0



W
X
Y
Z

A
2.706850
0.628133
0.907969
0.503826
B
0.651118
NaN
NaN
0.605965
C
NaN
0.740122
0.528813
NaN
D
0.188695
NaN
NaN
0.955057
E
0.190794
1.978757
2.605967
0.683509




df[df['W']>0]


W
X
Y
Z

A
2.706850
0.628133
0.907969
0.503826
B
0.651118
-0.319318
-0.848077
0.605965
D
0.188695
-0.758872
-0.933237
0.955057
E
0.190794
1.978757
2.605967
0.683509


df[df['W']>0][['Y','X']]



Y
X

A
0.907969
0.628133
B
-0.848077
-0.319318
D
-0.933237
-0.758872
E
2.605967
1.978757


df[(df['W']>0) & (df['Y'] > 1)]


W
X
Y
Z

E
0.190794
1.978757
2.605967
0.683509




# Reset to default 0,1...n index
df.reset_index()


index
W
X
Y
Z

0
A
2.706850
0.628133
0.907969
0.503826
1
B
0.651118
-0.319318
-0.848077
0.605965
2
C
-2.018168
0.740122
0.528813
-0.589001
3
D
0.188695
-0.758872
-0.933237
0.955057
4
E
0.190794
1.978757
2.605967
0.683509



newind = 'CA NY WY OR CO'.split()
df['States'] = newind
df


W
X
Y
Z
States

A
2.706850
0.628133
0.907969
0.503826
CA
B
0.651118
-0.319318
-0.848077
0.605965
NY
C
-2.018168
0.740122
0.528813
-0.589001
WY
D
0.188695
-0.758872
-0.933237
0.955057
OR
E
0.190794
1.978757
2.605967
0.683509
CO


df.set_index('States') # but this is temperoray ... to make permanent change 
df.set_index('States',inplace=True) #for permanent change








W
X
Y
Z
States


CA
2.706850
0.628133
0.907969
0.503826
NY
0.651118
-0.319318
-0.848077
0.605965
WY
-2.018168
0.740122
0.528813
-0.589001
OR
0.188695
-0.758872
-0.933237
0.955057
CO
0.190794
1.978757
2.605967
0.683509











In [215]:














Multi-Index and Index Hierarchy