Python Learning Note (iv): Pandas basics

Pandas Foundation Serise

importas pdfromimport= Series([4-753])obj

0    41   -72    53    3dtype: int64

obj.values

array([ 4, -7,  5,  3], dtype=int64)

obj.index

RangeIndex(start=0, stop=4, step=1)

obj[[1,3]]# 跳着选取数据

1   -73    3dtype: int64

obj[1:3]

1   -72    5dtype: int64

pd.isnull(obj)

0    False1    False2    False3    Falsedtype: bool

• Reindex can be used to interpolate values

obj.reindex(range(5='ffill')

0    41   -72    53    34    3dtype: int64

• Tag slices are closed intervals.

Dataframe

= {'state': ['asd','qwe','sdf','ert'],       'year': [2000200120022003],       'pop': [1.5,1.7,3.6,2.4= DataFrame(data)data

State Year

	Pop
0	1.5	Asd	2000
1	1.7	Qwe	2001
2	3.6	Sdf	2002
3	2.4	Ert	2003

data.year# 比r里提取列要方便点

0    20001    20012    20023    2003Name: year, dtype: int64

data['debt'=range(4)data

State Year

	Pop			Debt
0	1.5	Asd	2000	0
1	1.7	Qwe	2001	1
2	3.6	Sdf	2002	2
3	2.4	Ert	2003	3

• Index is not modifiable.

= data.indexa[1=6

---------------------------------------------------------------------------TypeError                                 Traceback (most recent call last)<ipython-input-9-57677294f950> in <module>()      1 a = data.index----> 2 a[1] = 6F:\Anaconda\lib\site-packages\pandas\core\indexes\base.py in __setitem__(self, key, value)   1668    1669     def __setitem__(self, key, value):-> 1670         raise TypeError("Index does not support mutable operations")   1671    1672     def __getitem__(self, key):TypeError: Index does not support mutable operations

data.columns

Index(['pop', 'state', 'year', 'debt'], dtype='object')

• . IX label indexing function, entering rows and columns
• No. IX can only select one of the columns or a row, not the column and row selection

data[:3]

State Year

	Pop			Debt
0	1.5	Asd	2000	0
1	1.7	Qwe	2001	1
2	3.6	Sdf	2002	2

data.ix[:,:3]

State Year

	Pop
0	1.5	Asd	2000
1	1.7	Qwe	2001
2	3.6	Sdf	2002
3	2.4	Ert	2003

• Delete a column with Drop,axis = 0 for the row, and 1 for the column
• The original data is unchanged after deletion

data.drop(0,axis=0)

State Year

	Pop			Debt
1	1.7	Qwe	2001	1
2	3.6	Sdf	2002	2
3	2.4	Ert	2003	3

data.drop('year', axis=1)

State

	Pop		Debt
0	1.5	Asd	0
1	1.7	Qwe	1
2	3.6	Sdf	2
3	2.4	Ert	3

data

State Year

	Pop			Debt
0	1.5	Asd	2000	0
1	1.7	Qwe	2001	1
2	3.6	Sdf	2002	2
3	2.4	Ert	2003	3

importas= DataFrame(np.arange(9).reshape(33))df

	0	1	2
0	0	1	2
1	3	4	5
2	6	7	8

• Applymap () can apply a function to each element of Dataframe
• Apply () can apply a function to each dimension array

df.applymap(lambda'%.2f'% x)

	0	1	2
0	0.00	1.00	2.00
1	3.00	4.00	5.00
2	6.00	7.00	8.00

data.sort_values(by='pop')# 对某一列排序

State Year

	Pop			Debt
0	1.5	Asd	2000	0
1	1.7	Qwe	2001	1
3	2.4	Ert	2003	3
2	3.6	Sdf	2002	2

data.describe()

Year

	Pop		Debt
Count	4.000000	4.000000	4.000000
Mean	2.300000	2001.500000	1.500000
Std	0.948683	1.290994	1.290994
Min	1.500000	2000.000000	0.000000
25%	1.650000	2000.750000	0.750000
50%	2.050000	2001.500000	1.500000
75%	2.700000	2002.250000	2.250000
Max	3.600000	2003.000000	3.000000

df.isin([1])

	0	1	2
0	False	True	False
1	False	False	False
2	False	False	False

• None, Nan will be treated as NA
• Df.shape without parentheses is equivalent to dim ()

df.shape

(3, 3)

• Dropna Delete missing values

df.ix[:1, :1=Nonedf

	0	1	2
0	NaN	NaN	2
1	NaN	NaN	5
2	6.0	7.0	8

• Fill missing values can call dictionary, do not add different values

df.fillna({0:111:22})

	0	1	2
0	11.0	22.0	2
1	11.0	22.0	5
2	6.0	7.0	8

df

	0	1	2
0	NaN	NaN	2
1	NaN	NaN	5
2	6.0	7.0	8

df.fillna({0:111:22}, inplace=True)

	0	1	2
0	11.0	22.0	2
1	11.0	22.0	5
2	6.0	7.0	8

df

	0	1	2
0	11.0	22.0	2
1	11.0	22.0	5
2	6.0	7.0	8

• InPlace modifying objects does not produce a copy

Python Learning Note (iv): Pandas basics