Pandas fundamental operations

import pandas as pd

In [3]:

df = pd.read_csv('C:/BigData/CSV/01_data.csv')

In [4]:

df

Out[4]:

	Name	Country	Age	Job
0	John	USA	31	Student
1	Sabre	France	33	Lawyer
2	Kim	Korea	28	Developer
3	Sato	Japan	40	Chef
4	Lee	Korea	36	Professor
5	Smith	USA	55	CEO
6	David	USA	48	Banker

In [5]:

type(df)

Out[5]:

pandas.core.frame.DataFrame

In [7]:

sr = df['Name']

In [8]:

type(sr)

Out[8]:

pandas.core.series.Series

In [10]:

df_Name = df['Name']

In [11]:

df_Name

Out[11]:

0     John
1    Sabre
2      Kim
3     Sato
4      Lee
5    Smith
6    David
Name: Name, dtype: object

In [12]:

df_Name = df[ ['Name', 'Country']]

In [13]:

df_Name

Out[13]:

	Name	Country
0	John	USA
1	Sabre	France
2	Kim	Korea
3	Sato	Japan
4	Lee	Korea
5	Smith	USA
6	David	USA

In [14]:

type(df_Name)

Out[14]:

pandas.core.frame.DataFrame

In [15]:

df.head()

Out[15]:

	Name	Country	Age	Job
0	John	USA	31	Student
1	Sabre	France	33	Lawyer
2	Kim	Korea	28	Developer
3	Sato	Japan	40	Chef
4	Lee	Korea	36	Professor

In [26]:

df[2 : 6]['Name']

Out[26]:

2      Kim
3     Sato
4      Lee
5    Smith
Name: Name, dtype: object

In [28]:

group = df.groupby('Country')

In [29]:

group['Age'].mean()

Out[29]:

Country
France    33.000000
Japan     40.000000
Korea     32.000000
USA       44.666667
Name: Age, dtype: float64

In [30]:

df = pd.read_csv('C:/BigData/iris.csv')

In [35]:

df.shape

Out[35]:

(150, 5)

In [36]:

df.head()

Out[36]:

	sepal.length	sepal.width	petal.length	petal.width	variety
0	5.1	3.5	1.4	0.2	Setosa
1	4.9	3.0	1.4	0.2	Setosa
2	4.7	3.2	1.3	0.2	Setosa
3	4.6	3.1	1.5	0.2	Setosa
4	5.0	3.6	1.4	0.2	Setosa

In [41]:

train_data = df.iloc[: , : -1]

In [43]:

train_data.head()

Out[43]:

	sepal.length	sepal.width	petal.length	petal.width
0	5.1	3.5	1.4	0.2
1	4.9	3.0	1.4	0.2
2	4.7	3.2	1.3	0.2
3	4.6	3.1	1.5	0.2
4	5.0	3.6	1.4	0.2

In [44]:

train_label = df.iloc[ : , [-1]]

In [45]:

train_label

Out[45]:

	variety
0	Setosa
1	Setosa
2	Setosa
3	Setosa
4	Setosa
5	Setosa
6	Setosa
7	Setosa
8	Setosa
9	Setosa
10	Setosa
11	Setosa
12	Setosa
13	Setosa
14	Setosa
15	Setosa
16	Setosa
17	Setosa
18	Setosa
19	Setosa
20	Setosa
21	Setosa
22	Setosa
23	Setosa
24	Setosa
25	Setosa
26	Setosa
27	Setosa
28	Setosa
29	Setosa
...	...
120	Virginica
121	Virginica
122	Virginica
123	Virginica
124	Virginica
125	Virginica
126	Virginica
127	Virginica
128	Virginica
129	Virginica
130	Virginica
131	Virginica
132	Virginica
133	Virginica
134	Virginica
135	Virginica
136	Virginica
137	Virginica
138	Virginica
139	Virginica
140	Virginica
141	Virginica
142	Virginica
143	Virginica
144	Virginica
145	Virginica
146	Virginica
147	Virginica
148	Virginica
149	Virginica

150 rows × 1 columns