Pandas学习笔记04
05 Jan 2018 |pandas学习笔记04
pandas基本函数
1. Series基本函数功能
| 编号 | 方法 | 描述 | | :—- | :—-: | :—- | | 1 | axes | 返回行坐标标签列表 | | 2 | dtype | 返回对象dtype | | 3 | empty | 若Series为空，返回true | | 4 | ndim | 根据定义1返回基本数据的维度数 | | 5 | size | 返回基本数据元素个数 | | 6 | values | 将Series返回为ndarray | | 7 | head() | 返回前n行 | | 8 | tail() | 返回倒数n行 |
创建一个Series，观察以上表格属性操作
import pandas as pd
import numpy as np
s = pd.Series(np.random.randn(4))
print s
0 -1.012016
1 0.288053
2 -0.342121
3 1.177446
dtype: float64
axes
import pandas as pd
import numpy as np
s = pd.Series(np.random.randn(4))
print "The axes are:"
print s.axes
The axes are:
[RangeIndex(start=0, stop=4, step=1)]
empty
import pandas as pd
import numpy as np
s = pd.Series(np.random.randn(4))
print "Is the Object empty?"
print s.empty
Is the Object empty?
False
ndim
import pandas as pd
import numpy as np
s = pd.Series(np.random.randn(4))
print s
print "The dimensions of the object:"
print s.ndim
0 -1.879772
1 -0.682344
2 1.697040
3 -0.983682
dtype: float64
The dimensions of the object:
1
size
import pandas as pd
import numpy as np
s = pd.Series(np.random.randn(2))
print s
print "The size of the object:"
print s.size
0 -0.387747
1 -0.240524
dtype: float64
The size of the object:
2
values
import pandas as pd
import numpy as np
# create a series with 4 random numbers
s = pd.Series(np.random.randn(4))
print s
print "The actual data series is:"
print s.values
0 -0.800398
1 -1.046600
2 0.224018
3 -0.919266
dtype: float64
The actual data series is:
[-0.80039821 -1.04660011 0.22401843 -0.91926567]
Head & Tail
import pandas as pd
import numpy as np
# create a series with 4 random numbers
s = pd.Series(np.random.randn(4))
print "The original series is:"
print s
print "The first two rows of the data series:"
print s.head(2)
print "The last two rows of the data series:"
print s.tail(2)
The original series is:
0 0.511740
1 -1.460081
2 1.757649
3 0.791180
dtype: float64
The first two rows of the data series:
0 0.511740
1 -1.460081
dtype: float64
The last two rows of the data series:
2 1.757649
3 0.791180
dtype: float64
2. DataFrame基本函数功能
| 编号 | 方法 | 描述 | | :—- | :—-: | :—- | | 1 | T | 转置行和列 | | 2 | axes | 返回带有行轴标签和列轴标签的列表作为唯一成员 | | 3 | dtypes | 返回此对象中的dtypes | | 4 | empty | 如果NDFrame完全是空的，则为真[无项目];如果任何轴的长度为0 | | 5 | ndim | 轴/数组维度的数量 | | 6 | shape | 返回表示DataFrame维度的元组 | | 7 | size | NDFrame中的元素数目 | | 8 | values | NDFrame的Numpy表示 | | 9 | head() | 返回前n行 | | 10 | tail() | 返回倒数n行 |
创建一个DataFrame，观察以上表格属性操作
import pandas as pd
import numpy as np
# create a dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack']),
'Age':pd.Series([25,26,25,23,30,29,23]),
'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8])}
# create a DataFrame
df = pd.DataFrame(d)
print "Our data series is:"
print df
Our data series is:
Age Name Rating
0 25 Tom 4.23
1 26 James 3.24
2 25 Ricky 3.98
3 23 Vin 2.56
4 30 Steve 3.20
5 29 Smith 4.60
6 23 Jack 3.80
转置
import pandas as pd
import numpy as np
# Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack']),
'Age':pd.Series([25,26,25,23,30,29,23]),
'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8])}
# create a DataFrame
df = pd.DataFrame(d)
print "The transpose of the data series is:"
print df.T
The transpose of the data series is:
0 1 2 3 4 5 6
Age 25 26 25 23 30 29 23
Name Tom James Ricky Vin Steve Smith Jack
Rating 4.23 3.24 3.98 2.56 3.2 4.6 3.8
axes
import pandas as pd
import numpy as np
# Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack']),
'Age':pd.Series([25,26,25,23,30,29,23]),
'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8])}
# create a DataFrame
df = pd.DataFrame(d)
print "Row axis labels and column axis labels are:"
print df
print df.axes
Row axis labels and column axis labels are:
Age Name Rating
0 25 Tom 4.23
1 26 James 3.24
2 25 Ricky 3.98
3 23 Vin 2.56
4 30 Steve 3.20
5 29 Smith 4.60
6 23 Jack 3.80
[RangeIndex(start=0, stop=7, step=1), Index([u'Age', u'Name', u'Rating'], dtype='object')]
dtype
import pandas as pd
import numpy as np
# Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack']),
'Age':pd.Series([25,26,25,23,30,29,23]),
'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8])}
# create a DataFrame
df = pd.DataFrame(d)
print "The data types of each column are:"
print df.dtypes
The data types of each column are:
Age int64
Name object
Rating float64
dtype: object
empty
import pandas as pd
import numpy as np
# Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack']),
'Age':pd.Series([25,26,25,23,30,29,23]),
'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8])}
# create a DataFrame
df = pd.DataFrame(d)
print "Is the object empty?"
print df.empty
Is the object empty?
False
ndim
import pandas as pd
import numpy as np
# Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack']),
'Age':pd.Series([25,26,25,23,30,29,23]),
'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8])}
# create a DataFrame
df = pd.DataFrame(d)
print "Our object is:"
print df
print "The dimension of the object is:"
print df.ndim
Our object is:
Age Name Rating
0 25 Tom 4.23
1 26 James 3.24
2 25 Ricky 3.98
3 23 Vin 2.56
4 30 Steve 3.20
5 29 Smith 4.60
6 23 Jack 3.80
The dimension of the object is:
2
shape
import pandas as pd
import numpy as np
# Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack']),
'Age':pd.Series([25,26,25,23,30,29,23]),
'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8])}
# create a DataFrame
df = pd.DataFrame(d)
print "Our object is:"
print df
print "The shape of the object is:"
print df.shape
Our object is:
Age Name Rating
0 25 Tom 4.23
1 26 James 3.24
2 25 Ricky 3.98
3 23 Vin 2.56
4 30 Steve 3.20
5 29 Smith 4.60
6 23 Jack 3.80
The shape of the object is:
(7, 3)
size
import pandas as pd
import numpy as np
# Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack']),
'Age':pd.Series([25,26,25,23,30,29,23]),
'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8])}
# create a DataFrame
df = pd.DataFrame(d)
print "Our object is:"
print df
print "The total number of elements in our object is:"
print df.size
Our object is:
Age Name Rating
0 25 Tom 4.23
1 26 James 3.24
2 25 Ricky 3.98
3 23 Vin 2.56
4 30 Steve 3.20
5 29 Smith 4.60
6 23 Jack 3.80
The total number of elements in our object is:
21
values
返回DataFrame中的实际数据作为NDarray
import pandas as pd
import numpy as np
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack']),
'Age':pd.Series([25,26,25,23,30,29,23]),
'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8])}
# create a DataFrame
df = pd.DataFrame(d)
print "Our object is:"
print df
print "The actual data in our data frame is:"
print df.values
Our object is:
Age Name Rating
0 25 Tom 4.23
1 26 James 3.24
2 25 Ricky 3.98
3 23 Vin 2.56
4 30 Steve 3.20
5 29 Smith 4.60
6 23 Jack 3.80
The actual data in our data frame is:
[[25L 'Tom' 4.23]
[26L 'James' 3.24]
[25L 'Ricky' 3.98]
[23L 'Vin' 2.56]
[30L 'Steve' 3.2]
[29L 'Smith' 4.6]
[23L 'Jack' 3.8]]
Head & Tail
import pandas as pd
import numpy as np
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack']),
'Age':pd.Series([25,26,25,23,30,29,23]),
'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8])}
# create a DataFrame
df = pd.DataFrame(d)
print "Our object is:"
print df
print "The first two rows of the data frame is:"
print df.head(2)
print "The last two rows of the data frame is:"
print df.tail(2)
Our object is:
Age Name Rating
0 25 Tom 4.23
1 26 James 3.24
2 25 Ricky 3.98
3 23 Vin 2.56
4 30 Steve 3.20
5 29 Smith 4.60
6 23 Jack 3.80
The first two rows of the data frame is:
Age Name Rating
0 25 Tom 4.23
1 26 James 3.24
The last two rows of the data frame is:
Age Name Rating
5 29 Smith 4.6
6 23 Jack 3.8
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