文章很长，如果短时间看不完那就收藏吧，总会用到的
import pandas as pd
ser1 = pd.Series([1.5, 2.5, 3, 4.5, 5.0, 6])
print(ser1)0 1.5
1 2.5
2 3.0
3 4.5
4 5.0
5 6.0
dtype: float64import pandas as pd
ser2 = pd.Series(["India", "Canada", "Germany"], name="Countries")
print(ser2)0 India
1 Canada
2 Germany
Name: Countries, dtype: objectimport pandas as pd
ser3 = pd.Series(["A"]*4)
print(ser3)0 A
1 A
2 A
3 A
dtype: objectimport pandas as pd
ser4 = pd.Series({"India": "New Delhi",
"Japan": "Tokyo",
"UK": "London"})
print(ser4)India New Delhi
Japan Tokyo
UK London
dtype: objectimport pandas as pd
import numpy as np
ser1 = pd.Series(np.linspace(1, 10, 5))
print(ser1)
ser2 = pd.Series(np.random.normal(size=5))
print(ser2)0 1.00
1 3.25
2 5.50
3 7.75
4 10.00
dtype: float64
0 -1.694452
1 -1.570006
2 1.713794
3 0.338292
4 0.803511
dtype: float64import pandas as pd
import numpy as np
ser1 = pd.Series({"India": "New Delhi",
"Japan": "Tokyo",
"UK": "London"})
print(ser1.values)
print(ser1.index)
print("\n")
ser2 = pd.Series(np.random.normal(size=5))
print(ser2.index)
print(ser2.values)['New Delhi' 'Tokyo' 'London']
Index(['India', 'Japan', 'UK'], dtype='object')
RangeIndex(start=0, stop=5, step=1)
[ 0.66265478 -0.72222211 0.3608642 1.40955436 1.3096732 ]import pandas as pd
values = ["India", "Canada", "Australia",
"Japan", "Germany", "France"]
code = ["IND", "CAN", "AUS", "JAP", "GER", "FRA"]
ser1 = pd.Series(values, index=code)
print(ser1)IND India
CAN Canada
AUS Australia
JAP Japan
GER Germany
FRA France
dtype: objectimport pandas as pd
values = ["India", "Canada", "Australia",
"Japan", "Germany", "France"]
code = ["IND", "CAN", "AUS", "JAP", "GER", "FRA"]
ser1 = pd.Series(values, index=code)
print(len(ser1))
print(ser1.shape)
print(ser1.size)6
(6,)
6import pandas as pd
values = ["India", "Canada", "Australia",
"Japan", "Germany", "France"]
code = ["IND", "CAN", "AUS", "JAP", "GER", "FRA"]
ser1 = pd.Series(values, index=code)
print("-----Head()-----")
print(ser1.head())
print("\n\n-----Head(2)-----")
print(ser1.head(2))-----Head()-----
IND India
CAN Canada
AUS Australia
JAP Japan
GER Germany
dtype: object
-----Head(2)-----
IND India
CAN Canada
dtype: objectimport pandas as pd
values = ["India", "Canada", "Australia",
"Japan", "Germany", "France"]
code = ["IND", "CAN", "AUS", "JAP", "GER", "FRA"]
ser1 = pd.Series(values, index=code)
print("-----Tail()-----")
print(ser1.tail())
print("\n\n-----Tail(2)-----")
print(ser1.tail(2))-----Tail()-----
CAN Canada
AUS Australia
JAP Japan
GER Germany
FRA France
dtype: object
-----Tail(2)-----
GER Germany
FRA France
dtype: objectimport pandas as pd
values = ["India", "Canada", "Australia",
"Japan", "Germany", "France"]
code = ["IND", "CAN", "AUS", "JAP", "GER", "FRA"]
ser1 = pd.Series(values, index=code)
print("-----Take()-----")
print(ser1.take([2, 4, 5]))-----Take()-----
AUS Australia
GER Germany
FRA France
dtype: objectimport pandas as pd
num = [000, 100, 200, 300, 400, 500, 600, 700, 800, 900]
idx = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J']
series = pd.Series(num, index=idx)
print("\n [2:2] \n")
print(series[2:4])
print("\n [1:6:2] \n")
print(series[1:6:2])
print("\n [:6] \n")
print(series[:6])
print("\n [4:] \n")
print(series[4:])
print("\n [:4:2] \n")
print(series[:4:2])
print("\n [4::2] \n")
print(series[4::2])
print("\n [::-1] \n")
print(series[::-1])[2:2]
C 200
D 300
dtype: int64
[1:6:2]
B 100
D 300
F 500
dtype: int64
[:6]
A 0
B 100
C 200
D 300
E 400
F 500
dtype: int64
[4:]
E 400
F 500
G 600
H 700
I 800
J 900
dtype: int64
[:4:2]
A 0
C 200
dtype: int64
[4::2]
E 400
G 600
I 800
dtype: int64
[::-1]
J 900
I 800
H 700
G 600
F 500
E 400
D 300
C 200
B 100
A 0
dtype: int64import pandas as pd
employees = pd.DataFrame({
'EmpCode': ['Emp001', 'Emp00'],
'Name': ['John Doe', 'William Spark'],
'Occupation': ['Chemist', 'Statistician'],
'Date Of Join': ['2018-01-25', '2018-01-26'],
'Age': [23, 24]})
print(employees)Age Date Of Join EmpCode Name Occupation
0 23 2018-01-25 Emp001 John Doe Chemist
1 24 2018-01-26 Emp00 William Spark Statisticianimport pandas as pd
employees = pd.DataFrame(
data={'Name': ['John Doe', 'William Spark'],
'Occupation': ['Chemist', 'Statistician'],
'Date Of Join': ['2018-01-25', '2018-01-26'],
'Age': [23, 24]},
index=['Emp001', 'Emp002'],
columns=['Name', 'Occupation', 'Date Of Join', 'Age'])
print(employees)Name Occupation Date Of Join Age
Emp001 John Doe Chemist 2018-01-25 23
Emp002 William Spark Statistician 2018-01-26 24import pandas as pd
employees = pd.DataFrame({
'EmpCode': ['Emp001', 'Emp00'],
'Name': ['John Doe', 'William Spark'],
'Occupation': ['Chemist', 'Statistician'],
'Date Of Join': ['2018-01-25', '2018-01-26'],
'Age': [23, 24]})
employees.columns = ['EmpCode', 'EmpName', 'EmpOccupation', 'EmpDOJ', 'EmpAge']
print(employees)EmpCode EmpName EmpOccupation EmpDOJ EmpAge
0 23 2018-01-25 Emp001 John Doe Chemist
1 24 2018-01-26 Emp00 William Spark Statisticianimport pandas as pd
employees = pd.DataFrame({
'EmpCode': ['Emp001', 'Emp002', 'Emp003', 'Emp004', 'Emp005'],
'Name': ['John', 'Doe', 'William', 'Spark', 'Mark'],
'Occupation': ['Chemist', 'Statistician', 'Statistician',
'Statistician', 'Programmer'],
'Date Of Join': ['2018-01-25', '2018-01-26', '2018-01-26', '2018-02-26',
'2018-03-16'],
'Age': [23, 24, 34, 29, 40]})
print("\nUse == operator\n")
print(employees.loc[employees['Age'] == 23])
print("\nUse < operator\n")
print(employees.loc[employees['Age'] < 30])
print("\nUse != operator\n")
print(employees.loc[employees['Occupation'] != 'Statistician'])
print("\nMultiple Conditions\n")
print(employees.loc[(employees['Occupation'] != 'Statistician') &
(employees['Name'] == 'John')])Use == operator
Age Date Of Join EmpCode Name Occupation
0 23 2018-01-25 Emp001 John Chemist
Use < operator
Age Date Of Join EmpCode Name Occupation
0 23 2018-01-25 Emp001 John Chemist
1 24 2018-01-26 Emp002 Doe Statistician
3 29 2018-02-26 Emp004 Spark Statistician
Use != operator
Age Date Of Join EmpCode Name Occupation
0 23 2018-01-25 Emp001 John Chemist
4 40 2018-03-16 Emp005 Mark Programmer
Multiple Conditions
Age Date Of Join EmpCode Name Occupation
0 23 2018-01-25 Emp001 John Chemistimport pandas as pd
employees = pd.DataFrame({
'EmpCode': ['Emp001', 'Emp002', 'Emp003', 'Emp004', 'Emp005'],
'Name': ['John', 'Doe', 'William', 'Spark', 'Mark'],
'Occupation': ['Chemist', 'Statistician', 'Statistician',
'Statistician', 'Programmer'],
'Date Of Join': ['2018-01-25', '2018-01-26', '2018-01-26', '2018-02-26',
'2018-03-16'],
'Age': [23, 24, 34, 29, 40]})
print("\nUse isin operator\n")
print(employees.loc[employees['Occupation'].isin(['Chemist','Programmer'])])
print("\nMultiple Conditions\n")
print(employees.loc[(employees['Occupation'] == 'Chemist') |
(employees['Name'] == 'John') &
(employees['Age'] < 30)])Use isin operator
Age Date Of Join EmpCode Name Occupation
0 23 2018-01-25 Emp001 John Chemist
4 40 2018-03-16 Emp005 Mark Programmer
Multiple Conditions
Age Date Of Join EmpCode Name Occupation
0 23 2018-01-25 Emp001 John Chemistimport pandas as pd
employees = pd.DataFrame({
'EmpCode': ['Emp001', 'Emp002', 'Emp003', 'Emp004', 'Emp005'],
'Name': ['John', 'Doe', 'William', 'Spark', 'Mark'],
'Occupation': ['Chemist', 'Statistician', 'Statistician',
'Statistician', 'Programmer'],
'Date Of Join': ['2018-01-25', '2018-01-26', '2018-01-26', '2018-02-26',
'2018-03-16'],
'Age': [23, 24, 34, 29, 40]})
print("\n Example iterrows \n")
for index, col in employees.iterrows():
print(col['Name'], "--", col['Age'])
print("\n Example itertuples \n")
for row in employees.itertuples(index=True, name='Pandas'):
print(getattr(row, "Name"), "--", getattr(row, "Age"))Example iterrows
John -- 23
Doe -- 24
William -- 34
Spark -- 29
Mark -- 40
Example itertuples
John -- 23
Doe -- 24
William -- 34
Spark -- 29
Mark -- 40import pandas as pd
employees = pd.DataFrame({
'EmpCode': ['Emp001', 'Emp002', 'Emp003', 'Emp004', 'Emp005'],
'Name': ['John', 'Doe', 'William', 'Spark', 'Mark'],
'Occupation': ['Chemist', 'Statistician', 'Statistician',
'Statistician', 'Programmer'],
'Date Of Join': ['2018-01-25', '2018-01-26', '2018-01-26', '2018-02-26',
'2018-03-16'],
'Age': [23, 24, 34, 29, 40]})
print(employees)
print("\n Drop Column by Name \n")
employees.drop('Age', axis=1, inplace=True)
print(employees)
print("\n Drop Column by Index \n")
employees.drop(employees.columns[[0,1]], axis=1, inplace=True)
print(employees)Age Date Of Join EmpCode Name Occupation
0 23 2018-01-25 Emp001 John Chemist
1 24 2018-01-26 Emp002 Doe Statistician
2 34 2018-01-26 Emp003 William Statistician
3 29 2018-02-26 Emp004 Spark Statistician
4 40 2018-03-16 Emp005 Mark Programmer
Drop Column by Name
Date Of Join EmpCode Name Occupation
0 2018-01-25 Emp001 John Chemist
1 2018-01-26 Emp002 Doe Statistician
2 2018-01-26 Emp003 William Statistician
3 2018-02-26 Emp004 Spark Statistician
4 2018-03-16 Emp005 Mark Programmer
Drop Column by Index
Name Occupation
0 John Chemist
1 Doe Statistician
2 William Statistician
3 Spark Statistician
4 Mark Programmerimport pandas as pd
employees = pd.DataFrame({
'EmpCode': ['Emp001', 'Emp002', 'Emp003', 'Emp004', 'Emp005'],
'Name': ['John', 'Doe', 'William', 'Spark', 'Mark'],
'Occupation': ['Chemist', 'Statistician', 'Statistician',
'Statistician', 'Programmer'],
'Date Of Join': ['2018-01-25', '2018-01-26', '2018-01-26', '2018-02-26',
'2018-03-16'],
'Age': [23, 24, 34, 29, 40]})
employees['City'] = ['London', 'Tokyo', 'Sydney', 'London', 'Toronto']
print(employees)Age Date Of Join EmpCode Name Occupation City
0 23 2018-01-25 Emp001 John Chemist London
1 24 2018-01-26 Emp002 Doe Statistician Tokyo
2 34 2018-01-26 Emp003 William Statistician Sydney
3 29 2018-02-26 Emp004 Spark Statistician London
4 40 2018-03-16 Emp005 Mark Programmer Torontoimport pandas as pd
employees = pd.DataFrame({
'EmpCode': ['Emp001', 'Emp002', 'Emp003', 'Emp004', 'Emp005'],
'Name': ['John', 'Doe', 'William', 'Spark', 'Mark'],
'Occupation': ['Chemist', 'Statistician', 'Statistician',
'Statistician', 'Programmer'],
'Date Of Join': ['2018-01-25', '2018-01-26', '2018-01-26', '2018-02-26',
'2018-03-16'],
'Age': [23, 24, 34, 29, 40]})
print(list(employees))
print(list(employees.columns.values))
print(employees.columns.tolist())['Age', 'Date Of Join', 'EmpCode', 'Name', 'Occupation']
['Age', 'Date Of Join', 'EmpCode', 'Name', 'Occupation']
['Age', 'Date Of Join', 'EmpCode', 'Name', 'Occupation']import pandas as pd
import numpy as np
np.random.seed(5)
df_random = pd.DataFrame(np.random.randint(100, size=(10, 6)),
columns=list('ABCDEF'),
index=['Row-{}'.format(i) for i in range(10)])
print(df_random)A B C D E F
Row-0 99 78 61 16 73 8
Row-1 62 27 30 80 7 76
Row-2 15 53 80 27 44 77
Row-3 75 65 47 30 84 86
Row-4 18 9 41 62 1 82
Row-5 16 78 5 58 0 80
Row-6 4 36 51 27 31 2
Row-7 68 38 83 19 18 7
Row-8 30 62 11 67 65 55
Row-9 3 91 78 27 29 33import pandas as pd
employees = pd.DataFrame({
'EmpCode': ['Emp001', 'Emp002', 'Emp003', 'Emp004', 'Emp005'],
'Name': ['John', 'Doe', 'William', 'Spark', 'Mark'],
'Occupation': ['Chemist', 'Statistician', 'Statistician',
'Statistician', 'Programmer'],
'Date Of Join': ['2018-01-25', '2018-01-26', '2018-01-26', '2018-02-26',
'2018-03-16'],
'Age': [23, 24, 34, 29, 40]})
df = employees[['EmpCode', 'Age', 'Name']]
print(df)EmpCode Age Name
0 Emp001 23 John
1 Emp002 24 Doe
2 Emp003 34 William
3 Emp004 29 Spark
4 Emp005 40 Markimport pandas as pd
data = ({'Age': [30, 20, 22, 40, 32, 28, 39],
'Color': ['Blue', 'Green', 'Red', 'White', 'Gray', 'Black',
'Red'],
'Food': ['Steak', 'Lamb', 'Mango', 'Apple', 'Cheese',
'Melon', 'Beans'],
'Height': [165, 70, 120, 80, 180, 172, 150],
'Score': [4.6, 8.3, 9.0, 3.3, 1.8, 9.5, 2.2],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
})
print(data)
df = pd.DataFrame(data)
print(df){'Height': [165, 70, 120, 80, 180, 172, 150], 'Food': ['Steak', 'Lamb', 'Mango',
'Apple', 'Cheese', 'Melon', 'Beans'], 'Age': [30, 20, 22, 40, 32, 28, 39], 'Sco
re': [4.6, 8.3, 9.0, 3.3, 1.8, 9.5, 2.2], 'Color': ['Blue', 'Green', 'Red', 'Whi
te', 'Gray', 'Black', 'Red'], 'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX'
]}
Age Color Food Height Score State
0 30 Blue Steak 165 4.6 NY
1 20 Green Lamb 70 8.3 TX
2 22 Red Mango 120 9.0 FL
3 40 White Apple 80 3.3 AL
4 32 Gray Cheese 180 1.8 AK
5 28 Black Melon 172 9.5 TX
6 39 Red Beans 150 2.2 TXimport pandas as pd
df = pd.DataFrame({'Age': [30, 20, 22, 40, 32, 28, 39],
'Color': ['Blue', 'Green', 'Red', 'White', 'Gray', 'Black',
'Red'],
'Food': ['Steak', 'Lamb', 'Mango', 'Apple', 'Cheese',
'Melon', 'Beans'],
'Height': [165, 70, 120, 80, 180, 172, 150],
'Score': [4.6, 8.3, 9.0, 3.3, 1.8, 9.5, 2.2],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean',
'Christina', 'Cornelia'])
print("\n -- Selecting a single row with .loc with a string -- \n")
print(df.loc['Penelope'])
print("\n -- Selecting multiple rows with .loc with a list of strings -- \n")
print(df.loc[['Cornelia', 'Jane', 'Dean']])
print("\n -- Selecting multiple rows with .loc with slice notation -- \n")
print(df.loc['Aaron':'Dean'])-- Selecting a single row with .loc with a string --
Age 40
Color White
Food Apple
Height 80
Score 3.3
State AL
Name: Penelope, dtype: object
-- Selecting multiple rows with .loc with a list of strings --
Age Color Food Height Score State
Cornelia 39 Red Beans 150 2.2 TX
Jane 30 Blue Steak 165 4.6 NY
Dean 32 Gray Cheese 180 1.8 AK
-- Selecting multiple rows with .loc with slice notation --
Age Color Food Height Score State
Aaron 22 Red Mango 120 9.0 FL
Penelope 40 White Apple 80 3.3 AL
Dean 32 Gray Cheese 180 1.8 AKimport pandas as pd
df = pd.DataFrame()
if df.empty:
print('DataFrame is empty!')DataFrame is empty!import pandas as pd
values = ["India", "Canada", "Australia",
"Japan", "Germany", "France"]
code = ["IND", "CAN", "AUS", "JAP", "GER", "FRA"]
df = pd.DataFrame(values, index=code, columns=['Country'])
print(df)Country
IND India
CAN Canada
AUS Australia
JAP Japan
GER Germany
FRA Franceimport pandas as pd
df = pd.DataFrame({'Age': [30, 20, 22, 40, 32, 28, 39],
'Color': ['Blue', 'Green', 'Red', 'White', 'Gray', 'Black',
'Red'],
'Food': ['Steak', 'Lamb', 'Mango', 'Apple', 'Cheese',
'Melon', 'Beans'],
'Height': [165, 70, 120, 80, 180, 172, 150],
'Score': [4.6, 8.3, 9.0, 3.3, 1.8, 9.5, 2.2],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean',
'Christina', 'Cornelia'])
print("\n -- Selecting a single row with .iloc with an integer -- \n")
print(df.iloc[4])
print("\n -- Selecting multiple rows with .iloc with a list of integers -- \n")
print(df.iloc[[2, -2]])
print("\n -- Selecting multiple rows with .iloc with slice notation -- \n")
print(df.iloc[:5:3])-- Selecting a single row with .iloc with an integer --
Age 32
Color Gray
Food Cheese
Height 180
Score 1.8
State AK
Name: Dean, dtype: object
-- Selecting multiple rows with .iloc with a list of integers --
Age Color Food Height Score State
Aaron 22 Red Mango 120 9.0 FL
Christina 28 Black Melon 172 9.5 TX
-- Selecting multiple rows with .iloc with slice notation --
Age Color Food Height Score State
Jane 30 Blue Steak 165 4.6 NY
Penelope 40 White Apple 80 3.3 ALimport pandas as pd
df = pd.DataFrame({'Age': [30, 20, 22, 40, 32, 28, 39],
'Color': ['Blue', 'Green', 'Red', 'White', 'Gray', 'Black',
'Red'],
'Food': ['Steak', 'Lamb', 'Mango', 'Apple', 'Cheese',
'Melon', 'Beans'],
'Height': [165, 70, 120, 80, 180, 172, 150],
'Score': [4.6, 8.3, 9.0, 3.3, 1.8, 9.5, 2.2],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean',
'Christina', 'Cornelia'])
print("\n -- loc -- \n")
print(df.loc[df['Age'] < 30, ['Color', 'Height']])
print("\n -- iloc -- \n")
print(df.iloc[(df['Age'] < 30).values, [1, 3]])-- loc --
Color Height
Nick Green 70
Aaron Red 120
Christina Black 172
-- iloc --
Color Height
Nick Green 70
Aaron Red 120
Christina Black 172import datetime
import pandas as pd
todays_date = datetime.datetime.now().date()
index = pd.date_range(todays_date, periods=10, freq='D')
columns = ['A', 'B', 'C']
df = pd.DataFrame(index=index, columns=columns)
df = df.fillna(0)
print(df)A B C
2018-09-30 0 0 0
2018-10-01 0 0 0
2018-10-02 0 0 0
2018-10-03 0 0 0
2018-10-04 0 0 0
2018-10-05 0 0 0
2018-10-06 0 0 0
2018-10-07 0 0 0
2018-10-08 0 0 0
2018-10-09 0 0 0import pandas as pd
df = pd.DataFrame({'Age': [30, 20, 22, 40, 32, 28, 39],
'Color': ['Blue', 'Green', 'Red', 'White', 'Gray', 'Black',
'Red'],
'Food': ['Steak', 'Lamb', 'Mango', 'Apple', 'Cheese',
'Melon', 'Beans'],
'Height': [165, 70, 120, 80, 180, 172, 150],
'Score': [4.6, 8.3, 9.0, 3.3, 1.8, 9.5, 2.2],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean',
'Christina', 'Cornelia'])
print("\n -- Change order using columns -- \n")
new_order = [3, 2, 1, 4, 5, 0]
df = df[df.columns[new_order]]
print(df)
print("\n -- Change order using reindex -- \n")
df = df.reindex(['State', 'Color', 'Age', 'Food', 'Score', 'Height'], axis=1)
print(df)-- Change order using columns --
Height Food Color Score State Age
Jane 165 Steak Blue 4.6 NY 30
Nick 70 Lamb Green 8.3 TX 20
Aaron 120 Mango Red 9.0 FL 22
Penelope 80 Apple White 3.3 AL 40
Dean 180 Cheese Gray 1.8 AK 32
Christina 172 Melon Black 9.5 TX 28
Cornelia 150 Beans Red 2.2 TX 39
-- Change order using reindex --
State Color Age Food Score Height
Jane NY Blue 30 Steak 4.6 165
Nick TX Green 20 Lamb 8.3 70
Aaron FL Red 22 Mango 9.0 120
Penelope AL White 40 Apple 3.3 80
Dean AK Gray 32 Cheese 1.8 180
Christina TX Black 28 Melon 9.5 172
Cornelia TX Red 39 Beans 2.2 150import pandas as pd
df = pd.DataFrame({'Age': [30, 20, 22, 40, 32, 28, 39],
'Color': ['Blue', 'Green', 'Red', 'White', 'Gray', 'Black',
'Red'],
'Food': ['Steak', 'Lamb', 'Mango', 'Apple', 'Cheese',
'Melon', 'Beans'],
'Height': [165, 70, 120, 80, 180, 172, 150],
'Score': [4.6, 8.3, 9.0, 3.3, 1.8, 9.5, 2.2],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean',
'Christina', 'Cornelia'])
print(df.dtypes)Age int64
Color object
Food object
Height int64
Score float64
State object
dtype: objectimport pandas as pd
df = pd.DataFrame({'Age': [30, 20, 22, 40, 32, 28, 39],
'Color': ['Blue', 'Green', 'Red', 'White', 'Gray', 'Black',
'Red'],
'Food': ['Steak', 'Lamb', 'Mango', 'Apple', 'Cheese',
'Melon', 'Beans'],
'Height': [165, 70, 120, 80, 180, 172, 150],
'Score': [4.6, 8.3, 9.0, 3.3, 1.8, 9.5, 2.2],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean',
'Christina', 'Cornelia'])
print(df.dtypes)
df['Age'] = df['Age'].astype(str)
print(df.dtypes)Age int64
Color object
Food object
Height int64
Score float64
State object
dtype: object
Age object
Color object
Food object
Height int64
Score float64
State object
dtype: objectimport pandas as pd
df = pd.DataFrame({'DateOFBirth': [1349720105, 1349806505, 1349892905,
1349979305, 1350065705, 1349792905,
1349730105],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean',
'Christina', 'Cornelia'])
print("\n----------------Before---------------\n")
print(df.dtypes)
print(df)
df['DateOFBirth'] = pd.to_datetime(df['DateOFBirth'], unit='s')
print("\n----------------After----------------\n")
print(df.dtypes)
print(df)----------------Before---------------
DateOFBirth int64
State object
dtype: object
DateOFBirth State
Jane 1349720105 NY
Nick 1349806505 TX
Aaron 1349892905 FL
Penelope 1349979305 AL
Dean 1350065705 AK
Christina 1349792905 TX
Cornelia 1349730105 TX
----------------After----------------
DateOFBirth datetime64[ns]
State object
dtype: object
DateOFBirth State
Jane 2012-10-08 18:15:05 NY
Nick 2012-10-09 18:15:05 TX
Aaron 2012-10-10 18:15:05 FL
Penelope 2012-10-11 18:15:05 AL
Dean 2012-10-12 18:15:05 AK
Christina 2012-10-09 14:28:25 TX
Cornelia 2012-10-08 21:01:45 TXimport pandas as pd
df = pd.DataFrame({'DailyExp': [75.7, 56.69, 55.69, 96.5, 84.9, 110.5,
58.9],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean',
'Christina', 'Cornelia'])
print("\n----------------Before---------------\n")
print(df.dtypes)
print(df)
df['DailyExp'] = df['DailyExp'].astype(int)
print("\n----------------After----------------\n")
print(df.dtypes)
print(df)----------------Before---------------
DailyExp float64
State object
dtype: object
DailyExp State
Jane 75.70 NY
Nick 56.69 TX
Aaron 55.69 FL
Penelope 96.50 AL
Dean 84.90 AK
Christina 110.50 TX
Cornelia 58.90 TX
----------------After----------------
DailyExp int32
State object
dtype: object
DailyExp State
Jane 75 NY
Nick 56 TX
Aaron 55 FL
Penelope 96 AL
Dean 84 AK
Christina 110 TX
Cornelia 58 TXimport pandas as pd
df = pd.DataFrame({'DateOfBirth': ['1986-11-11', '1999-05-12', '1976-01-01',
'1986-06-01', '1983-06-04', '1990-03-07',
'1999-07-09'],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean',
'Christina', 'Cornelia'])
print("\n----------------Before---------------\n")
print(df.dtypes)
df['DateOfBirth'] = df['DateOfBirth'].astype('datetime64')
print("\n----------------After----------------\n")
print(df.dtypes)----------------Before---------------
DateOfBirth object
State object
dtype: object
----------------After----------------
DateOfBirth datetime64[ns]
State object
dtype: objectimport pandas as pd
df1 = pd.DataFrame({'Age': [30, 20, 22, 40], 'Height': [165, 70, 120, 80],
'Score': [4.6, 8.3, 9.0, 3.3], 'State': ['NY', 'TX',
'FL', 'AL']},
index=['Jane', 'Nick', 'Aaron', 'Penelope'])
df2 = pd.DataFrame({'Age': [32, 28, 39], 'Color': ['Gray', 'Black', 'Red'],
'Food': ['Cheese', 'Melon', 'Beans'],
'Score': [1.8, 9.5, 2.2], 'State': ['AK', 'TX', 'TX']},
index=['Dean', 'Christina', 'Cornelia'])
df3 = df1.append(df2, sort=True)
print(df3)Age Color Food Height Score State
Jane 30 NaN NaN 165.0 4.6 NY
Nick 20 NaN NaN 70.0 8.3 TX
Aaron 22 NaN NaN 120.0 9.0 FL
Penelope 40 NaN NaN 80.0 3.3 AL
Dean 32 Gray Cheese NaN 1.8 AK
Christina 28 Black Melon NaN 9.5 TX
Cornelia 39 Red Beans NaN 2.2 TXimport pandas as pd
employees = pd.DataFrame({
'EmpCode': ['Emp001', 'Emp002', 'Emp003', 'Emp004', 'Emp005'],
'Name': ['John', 'Doe', 'William', 'Spark', 'Mark'],
'Occupation': ['Chemist', 'Statistician', 'Statistician',
'Statistician', 'Programmer'],
'Date Of Join': ['2018-01-25', '2018-01-26', '2018-01-26', '2018-02-26',
'2018-03-16'],
'Age': [23, 24, 34, 29, 40]})
print("\n------------ BEFORE ----------------\n")
print(employees)
employees.loc[len(employees)] = [45, '2018-01-25', 'Emp006', 'Sunny',
'Programmer']
print("\n------------ AFTER ----------------\n")
print(employees)------------ BEFORE ----------------
Age Date Of Join EmpCode Name Occupation
0 23 2018-01-25 Emp001 John Chemist
1 24 2018-01-26 Emp002 Doe Statistician
2 34 2018-01-26 Emp003 William Statistician
3 29 2018-02-26 Emp004 Spark Statistician
4 40 2018-03-16 Emp005 Mark Programmer
------------ AFTER ----------------
Age Date Of Join EmpCode Name Occupation
0 23 2018-01-25 Emp001 John Chemist
1 24 2018-01-26 Emp002 Doe Statistician
2 34 2018-01-26 Emp003 William Statistician
3 29 2018-02-26 Emp004 Spark Statistician
4 40 2018-03-16 Emp005 Mark Programmer
5 45 2018-01-25 Emp006 Sunny Programmerimport pandas as pd
employees = pd.DataFrame(
data={'Name': ['John Doe', 'William Spark'],
'Occupation': ['Chemist', 'Statistician'],
'Date Of Join': ['2018-01-25', '2018-01-26'],
'Age': [23, 24]},
index=['Emp001', 'Emp002'],
columns=['Name', 'Occupation', 'Date Of Join', 'Age'])
print("\n------------ BEFORE ----------------\n")
print(employees)
employees.loc['Emp003'] = ['Sunny', 'Programmer', '2018-01-25', 45]
print("\n------------ AFTER ----------------\n")
print(employees)------------ BEFORE ----------------
Name Occupation Date Of Join Age
Emp001 John Doe Chemist 2018-01-25 23
Emp002 William Spark Statistician 2018-01-26 24
------------ AFTER ----------------
Name Occupation Date Of Join Age
Emp001 John Doe Chemist 2018-01-25 23
Emp002 William Spark Statistician 2018-01-26 24
Emp003 Sunny Programmer 2018-01-25 45import pandas as pd
cols = ['Zip']
lst = []
zip = 32100
for a in range(10):
lst.append([zip])
zip = zip + 1
df = pd.DataFrame(lst, columns=cols)
print(df)Zip
0 32100
1 32101
2 32102
3 32103
4 32104
5 32105
6 32106
7 32107
8 32108
9 32109import pandas as pd
employees = pd.DataFrame({
'EmpCode': ['Emp002', 'Emp003', 'Emp004'],
'Name': ['John', 'Doe', 'William'],
'Occupation': ['Chemist', 'Statistician', 'Statistician'],
'Date Of Join': ['2018-01-25', '2018-01-26', '2018-01-26'],
'Age': [23, 24, 34]})
print("\n------------ BEFORE ----------------\n")
print(employees)
# New line
line = pd.DataFrame({'Name': 'Dean', 'Age': 45, 'EmpCode': 'Emp001',
'Date Of Join': '2018-02-26', 'Occupation': 'Chemist'
}, index=[0])
# Concatenate two dataframe
employees = pd.concat([line,employees.ix[:]]).reset_index(drop=True)
print("\n------------ AFTER ----------------\n")
print(employees)------------ BEFORE ----------------
Age Date Of Join EmpCode Name Occupation
0 23 2018-01-25 Emp002 John Chemist
1 24 2018-01-26 Emp003 Doe Statistician
2 34 2018-01-26 Emp004 William Statistician
------------ AFTER ----------------
Age Date Of Join EmpCode Name Occupation
0 45 2018-02-26 Emp001 Dean Chemist
1 23 2018-01-25 Emp002 John Chemist
2 24 2018-01-26 Emp003 Doe Statistician
3 34 2018-01-26 Emp004 William Statisticianimport pandas as pd
df = pd.DataFrame(columns=['Name', 'Age'])
df.loc[1, 'Name'] = 'Rocky'
df.loc[1, 'Age'] = 23
df.loc[2, 'Name'] = 'Sunny'
print(df)Name Age
1 Rocky 23
2 Sunny NaNimport pandas as pd
df = pd.DataFrame(columns=['Name', 'Age'])
df.loc[1, 'Name'] = 'Rocky'
df.loc[1, 'Age'] = 21
df.loc[2, 'Name'] = 'Sunny'
df.loc[2, 'Age'] = 22
df.loc[3, 'Name'] = 'Mark'
df.loc[3, 'Age'] = 25
df.loc[4, 'Name'] = 'Taylor'
df.loc[4, 'Age'] = 28
print("\n------------ BEFORE ----------------\n")
print(df)
line = pd.DataFrame({"Name": "Jack", "Age": 24}, index=[2.5])
df = df.append(line, ignore_index=False)
df = df.sort_index().reset_index(drop=True)
df = df.reindex(['Name', 'Age'], axis=1)
print("\n------------ AFTER ----------------\n")
print(df)------------ BEFORE ----------------
Name Age
1 Rocky 21
2 Sunny 22
3 Mark 25
4 Taylor 28
------------ AFTER ----------------
Name Age
0 Rocky 21
1 Sunny 22
2 Jack 24
3 Mark 25
4 Taylor 28import pandas as pd
df = pd.DataFrame(columns=['Name', 'Age'])
df.loc['2014-05-01 18:47:05', 'Name'] = 'Rocky'
df.loc['2014-05-01 18:47:05', 'Age'] = 21
df.loc['2014-05-02 18:47:05', 'Name'] = 'Sunny'
df.loc['2014-05-02 18:47:05', 'Age'] = 22
df.loc['2014-05-03 18:47:05', 'Name'] = 'Mark'
df.loc['2014-05-03 18:47:05', 'Age'] = 25
print("\n------------ BEFORE ----------------\n")
print(df)
line = pd.to_datetime("2014-05-01 18:50:05", format="%Y-%m-%d %H:%M:%S")
new_row = pd.DataFrame([['Bunny', 26]], columns=['Name', 'Age'], index=[line])
df = pd.concat([df, pd.DataFrame(new_row)], ignore_index=False)
print("\n------------ AFTER ----------------\n")
print(df)------------ BEFORE ----------------
Name Age
2014-05-01 18:47:05 Rocky 21
2014-05-02 18:47:05 Sunny 22
2014-05-03 18:47:05 Mark 25
------------ AFTER ----------------
Name Age
2014-05-01 18:47:05 Rocky 21
2014-05-02 18:47:05 Sunny 22
2014-05-03 18:47:05 Mark 25
2014-05-01 18:50:05 Bunny 26import pandas as pd
a = {'A': 10, 'B': 20}
b = {'B': 30, 'C': 40, 'D': 50}
df1 = pd.DataFrame(a, index=[0])
df2 = pd.DataFrame(b, index=[1])
df = pd.DataFrame()
df = df.append(df1)
df = df.append(df2).fillna(0)
print(df)A B C D
0 10.0 20 0.0 0.0
1 0.0 30 40.0 50.0import pandas as pd
a = {'A': 10, 'B': 20}
b = {'B': 30, 'C': 40, 'D': 50}
df1 = pd.DataFrame(a, index=[0])
df2 = pd.DataFrame(b, index=[1])
d1 = pd.DataFrame()
d1 = d1.append(df1)
d1 = d1.append(df2).fillna(0)
print("\n------------ append ----------------\n")
print(d1)
d2 = pd.concat([df1, df2]).fillna(0)
print("\n------------ concat ----------------\n")
print(d2)
d3 = pd.DataFrame()
d3 = d3.combine_first(df1).combine_first(df2).fillna(0)
print("\n------------ combine_first ----------------\n")
print(d3)------------ append ----------------
A B C D
0 10.0 20 0.0 0.0
1 0.0 30 40.0 50.0
------------ concat ----------------
A B C D
0 10.0 20 0.0 0.0
1 0.0 30 40.0 50.0
------------ combine_first ----------------
A B C D
0 10.0 20.0 0.0 0.0
1 0.0 30.0 40.0 50.0import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [5, 5, 0, 0]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3'])
df['Mean Basket'] = df.mean(axis=1)
df.loc['Mean Fruit'] = df.mean()
print(df)Apple Orange Banana Pear Mean Basket
Basket1 10.000000 20.0 30.0 40.000000 25.0
Basket2 7.000000 14.0 21.0 28.000000 17.5
Basket3 5.000000 5.0 0.0 0.000000 2.5
Mean Fruit 7.333333 13.0 17.0 22.666667 15.0import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [5, 5, 0, 0]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3'])
df['Sum Basket'] = df.sum(axis=1)
df.loc['Sum Fruit'] = df.sum()
print(df)Apple Orange Banana Pear Sum Basket
Basket1 10 20 30 40 100
Basket2 7 14 21 28 70
Basket3 5 5 0 0 10
Sum Fruit 22 39 51 68 180import pandas as pd
df = pd.DataFrame(columns=['Name', 'Age'])
df.loc[1, 'Name'] = 'Rocky'
df.loc[1, 'Age'] = 21
df.loc[2, 'Name'] = 'Sunny'
df.loc[2, 'Age'] = 22
df.loc[3, 'Name'] = 'Mark'
df.loc[3, 'Age'] = 25
df.loc[4, 'Name'] = 'Taylor'
df.loc[4, 'Age'] = 28
print('\n------------ BEFORE ----------------\n')
print(df)
df['Employee'] = df['Name'].map(str) + ' - ' + df['Age'].map(str)
df = df.reindex(['Employee'], axis=1)
print('\n------------ AFTER ----------------\n')
print(df)------------ BEFORE ----------------
Name Age
1 Rocky 21
2 Sunny 22
3 Mark 25
4 Taylor 28
------------ AFTER ----------------
Employee
1 Rocky - 21
2 Sunny - 22
3 Mark - 25
4 Taylor - 28import pandas as pd
df = pd.DataFrame({'DateOfBirth': ['1986-11-11', '1999-05-12', '1976-01-01',
'1986-06-01', '1983-06-04', '1990-03-07',
'1999-07-09'],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean',
'Christina', 'Cornelia'])
print(df)
print("\n---- Filter with State contains TX ----\n")
df1 = df[df['State'].str.contains("TX")]
print(df1)DateOfBirth State
Jane 1986-11-11 NY
Nick 1999-05-12 TX
Aaron 1976-01-01 FL
Penelope 1986-06-01 AL
Dean 1983-06-04 AK
Christina 1990-03-07 TX
Cornelia 1999-07-09 TX
---- Filter with State contains TX ----
DateOfBirth State
Nick 1999-05-12 TX
Christina 1990-03-07 TX
Cornelia 1999-07-09 TXimport pandas as pd
df = pd.DataFrame({'DateOfBirth': ['1986-11-11', '1999-05-12', '1976-01-01',
'1986-06-01', '1983-06-04', '1990-03-07',
'1999-07-09'],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Pane', 'Aaron', 'Penelope', 'Frane',
'Christina', 'Cornelia'])
print(df)
print("\n---- Filter Index contains ane ----\n")
df.index = df.index.astype('str')
df1 = df[df.index.str.contains('ane')]
print(df1)DateOfBirth State
Jane 1986-11-11 NY
Pane 1999-05-12 TX
Aaron 1976-01-01 FL
Penelope 1986-06-01 AL
Frane 1983-06-04 AK
Christina 1990-03-07 TX
Cornelia 1999-07-09 TX
---- Filter Index contains ane ----
DateOfBirth State
Jane 1986-11-11 NY
Pane 1999-05-12 TX
Frane 1983-06-04 AKimport pandas as pd
df = pd.DataFrame({'DateOfBirth': ['1986-11-11', '1999-05-12', '1976-01-01',
'1986-06-01', '1983-06-04', '1990-03-07',
'1999-07-09'],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Pane', 'Aaron', 'Penelope', 'Frane',
'Christina', 'Cornelia'])
print(df)
print("\n---- Filter DataFrame using & ----\n")
df.index = df.index.astype('str')
df1 = df[df.index.str.contains('ane') & df['State'].str.contains("TX")]
print(df1)DateOfBirth State
Jane 1986-11-11 NY
Pane 1999-05-12 TX
Aaron 1976-01-01 FL
Penelope 1986-06-01 AL
Frane 1983-06-04 AK
Christina 1990-03-07 TX
Cornelia 1999-07-09 TX
---- Filter DataFrame using & ----
DateOfBirth State
Pane 1999-05-12 TXimport pandas as pd
df = pd.DataFrame({'DateOfBirth': ['1986-11-11', '1999-05-12', '1976-01-01',
'1986-06-01', '1983-06-04', '1990-03-07',
'1999-07-09'],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Pane', 'Aaron', 'Penelope', 'Frane',
'Christina', 'Cornelia'])
print(df)
print("\n---- Filter DataFrame using & ----\n")
df.index = df.index.astype('str')
df1 = df[df.index.str.contains('ane') | df['State'].str.contains("TX")]
print(df1)DateOfBirth State
Jane 1986-11-11 NY
Pane 1999-05-12 TX
Aaron 1976-01-01 FL
Penelope 1986-06-01 AL
Frane 1983-06-04 AK
Christina 1990-03-07 TX
Cornelia 1999-07-09 TX
---- Filter DataFrame using & ----
DateOfBirth State
Jane 1986-11-11 NY
Pane 1999-05-12 TX
Frane 1983-06-04 AK
Christina 1990-03-07 TX
Cornelia 1999-07-09 TXimport pandas as pd
import numpy as np
df = pd.DataFrame({
'EmpCode': ['Emp001', 'Emp002', 'Emp003', 'Emp004', 'Emp005'],
'Name': ['John', 'Doe', 'William', 'Spark', 'Mark'],
'Occupation': ['Chemist', 'Accountant', 'Statistician',
'Statistician', 'Programmer'],
'Date Of Join': ['2018-01-25', '2018-01-26', '2018-01-26', '2018-02-26',
'2018-03-16'],
'Age': [23, 24, 34, 29, 40]})
df['Department'] = pd.np.where(df.Occupation.str.contains("Chemist"), "Science",
pd.np.where(df.Occupation.str.contains("Statistician"), "Economics",
pd.np.where(df.Occupation.str.contains("Programmer"), "Computer", "General")))
print(df)Age Date Of Join EmpCode Name Occupation Department
0 23 2018-01-25 Emp001 John Chemist Science
1 24 2018-01-26 Emp002 Doe Accountant General
2 34 2018-01-26 Emp003 William Statistician Economics
3 29 2018-02-26 Emp004 Spark Statistician Economics
4 40 2018-03-16 Emp005 Mark Programmer Computerimport pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [5, 5, 0, 0],
[6, 6, 6, 6], [8, 8, 8, 8], [5, 5, 0, 0]],
columns=['Apple', 'Orange', 'Rice', 'Oil'],
index=['Basket1', 'Basket2', 'Basket3',
'Basket4', 'Basket5', 'Basket6'])
print(df)
print("\n ----------------------------- \n")
print(df[['Apple', 'Orange', 'Rice', 'Oil']].
groupby(['Apple']).agg(['mean', 'count']))Apple Orange Rice Oil
Basket1 10 20 30 40
Basket2 7 14 21 28
Basket3 5 5 0 0
Basket4 6 6 6 6
Basket5 8 8 8 8
Basket6 5 5 0 0
-----------------------------
Orange Rice Oil
mean count mean count mean count
Apple
5 5 2 0 2 0 2
6 6 1 6 1 6 1
7 14 1 21 1 28 1
8 8 1 8 1 8 1
10 20 1 30 1 40 1import pandas as pd
df = pd.DataFrame({'DateOfBirth': ['1986-11-11', '1999-05-12', '1976-01-01',
'1986-06-01', '1983-06-04', '1990-03-07',
'1999-07-09'],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Pane', 'Aaron', 'Penelope', 'Frane',
'Christina', 'Cornelia'])
if df['State'].str.contains('TX').any():
print("TX is there")TX is thereimport pandas as pd
df = pd.DataFrame({'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean',
'Christina', 'Cornelia'])
print(df)
print("\n----------------\n")
print(df["State"].unique())State
Jane NY
Nick TX
Aaron FL
Penelope AL
Dean AK
Christina TX
Cornelia TX
----------------
['NY' 'TX' 'FL' 'AL' 'AK']import pandas as pd
df = pd.DataFrame({'Age': [30, 20, 22, 40, 20, 30, 20, 25],
'Height': [165, 70, 120, 80, 162, 72, 124, 81],
'Score': [4.6, 8.3, 9.0, 3.3, 4, 8, 9, 3],
'State': ['NY', 'TX', 'FL', 'AL', 'NY', 'TX', 'FL', 'AL']},
index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Jaane', 'Nicky', 'Armour', 'Ponting'])
print(df.Age.value_counts())20 3
30 2
25 1
22 1
40 1
Name: Age, dtype: int64import pandas as pd
df = pd.DataFrame({'Age': [30, 30, 22, 40, 20, 30, 20, 25],
'Height': [165, 165, 120, 80, 162, 72, 124, 81],
'Score': [4.6, 4.6, 9.0, 3.3, 4, 8, 9, 3],
'State': ['NY', 'NY', 'FL', 'AL', 'NY', 'TX', 'FL', 'AL']},
index=['Jane', 'Jane', 'Aaron', 'Penelope', 'Jaane', 'Nicky',
'Armour', 'Ponting'])
print("\n -------- Duplicate Rows ----------- \n")
print(df)
df1 = df.reset_index().drop_duplicates(subset='index',
keep='first').set_index('index')
print("\n ------- Unique Rows ------------ \n")
print(df1)-------- Duplicate Rows -----------
Age Height Score State
Jane 30 165 4.6 NY
Jane 30 165 4.6 NY
Aaron 22 120 9.0 FL
Penelope 40 80 3.3 AL
Jaane 20 162 4.0 NY
Nicky 30 72 8.0 TX
Armour 20 124 9.0 FL
Ponting 25 81 3.0 AL
------- Unique Rows ------------
Age Height Score State
index
Jane 30 165 4.6 NY
Aaron 22 120 9.0 FL
Penelope 40 80 3.3 AL
Jaane 20 162 4.0 NY
Nicky 30 72 8.0 TX
Armour 20 124 9.0 FL
Ponting 25 81 3.0 ALimport pandas as pd
df = pd.DataFrame({'Age': [30, 40, 30, 40, 30, 30, 20, 25],
'Height': [120, 162, 120, 120, 120, 72, 120, 81],
'Score': [4.6, 4.6, 9.0, 3.3, 4, 8, 9, 3],
'State': ['NY', 'NY', 'FL', 'AL', 'NY', 'TX', 'FL', 'AL']},
index=['Jane', 'Jane', 'Aaron', 'Penelope', 'Jaane', 'Nicky',
'Armour', 'Ponting'])
print("\n -------- Duplicate Rows ----------- \n")
print(df)
df1 = df.reset_index().drop_duplicates(subset=['Age','Height'],
keep='first').set_index('index')
print("\n ------- Unique Rows ------------ \n")
print(df1)-------- Duplicate Rows -----------
Age Height Score State
Jane 30 120 4.6 NY
Jane 40 162 4.6 NY
Aaron 30 120 9.0 FL
Penelope 40 120 3.3 AL
Jaane 30 120 4.0 NY
Nicky 30 72 8.0 TX
Armour 20 120 9.0 FL
Ponting 25 81 3.0 AL
------- Unique Rows ------------
Age Height Score State
index
Jane 30 120 4.6 NY
Jane 40 162 4.6 NY
Penelope 40 120 3.3 AL
Nicky 30 72 8.0 TX
Armour 20 120 9.0 FL
Ponting 25 81 3.0 ALimport pandas as pd
df = pd.DataFrame({'Age': [30, 40, 30, 40, 30, 30, 20, 25],
'Height': [120, 162, 120, 120, 120, 72, 120, 81],
'Score': [4.6, 4.6, 9.0, 3.3, 4, 8, 9, 3],
'State': ['NY', 'NY', 'FL', 'AL', 'NY', 'TX', 'FL', 'AL']},
index=['Jane', 'Jane', 'Aaron', 'Penelope', 'Jaane', 'Nicky',
'Armour', 'Ponting'])
print(df.loc['Nicky', 'Age'])30import pandas as pd
df = pd.DataFrame({'Age': [30, 40, 30, 40, 30, 30, 20, 25],
'Height': [120, 162, 120, 120, 120, 72, 120, 81],
'Score': [4.6, 4.6, 9.0, 3.3, 4, 8, 9, 3],
'State': ['NY', 'NY', 'FL', 'AL', 'NY', 'TX', 'FL', 'AL']},
index=['Jane', 'Jane', 'Aaron', 'Penelope', 'Jaane', 'Nicky',
'Armour', 'Ponting'])
print("\nGet Height where Age is 20")
print(df.loc[df['Age'] == 20, 'Height'].values[0])
print("\nGet State where Age is 30")
print(df.loc[df['Age'] == 30, 'State'].values[0])Get Height where Age is 20
120
Get State where Age is 30
NYimport pandas as pd
df = pd.DataFrame({'Age': [30, 40, 30, 40, 30, 30, 20, 25],
'Height': [120, 162, 120, 120, 120, 72, 120, 81]},
index=['Jane', 'Jane', 'Aaron', 'Penelope', 'Jaane', 'Nicky',
'Armour', 'Ponting'])
print("\n--------------Before------------\n")
print(df)
df.iat[0, 0] = 90
df.iat[0, 1] = 91
df.iat[1, 1] = 92
df.iat[2, 1] = 93
df.iat[7, 1] = 99
print("\n--------------After------------\n")
print(df)--------------Before------------
Age Height
Jane 30 120
Jane 40 162
Aaron 30 120
Penelope 40 120
Jaane 30 120
Nicky 30 72
Armour 20 120
Ponting 25 81
--------------After------------
Age Height
Jane 90 91
Jane 40 92
Aaron 30 93
Penelope 40 120
Jaane 30 120
Nicky 30 72
Armour 20 120
Ponting 25 99import pandas as pd
df = pd.DataFrame({'Age': [30, 40, 30, 40, 30, 30, 20, 25],
'Height': [120, 162, 120, 120, 120, 72, 120, 81]},
index=['Jane', 'Jane', 'Aaron', 'Penelope', 'Jaane', 'Nicky',
'Armour', 'Ponting'])
print(df.loc[df.Age == 30,'Height'].tolist())[120, 120, 120, 72]import pandas as pd
df = pd.DataFrame({'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean',
'Christina', 'Cornelia'])
print(df)
dict = {"NY": 1, "TX": 2, "FL": 3, "AL": 4, "AK": 5}
df1 = df.replace({"State": dict})
print("\n\n")
print(df1)State
Jane NY
Nick TX
Aaron FL
Penelope AL
Dean AK
Christina TX
Cornelia TX
State
Jane 1
Nick 2
Aaron 3
Penelope 4
Dean 5
Christina 2
Cornelia 2import pandas as pd
df = pd.DataFrame({'DateOfBirth': ['1986-11-11', '1999-05-12', '1976-01-01',
'1986-06-01', '1983-06-04', '1990-03-07',
'1999-07-09'],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean',
'Christina', 'Cornelia'])
print(df.groupby('State').DateOfBirth.nunique())State
AK 1
AL 1
FL 1
NY 1
TX 3
Name: DateOfBirth, dtype: int64import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [5,]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3'])
print("\n--------- DataFrame ---------\n")
print(df)
print("\n--------- Use of isnull() ---------\n")
print(df.isnull())
print("\n--------- Use of notnull() ---------\n")
print(df.notnull())--------- DataFrame ---------
Apple Orange Banana Pear
Basket1 10 20.0 30.0 40.0
Basket2 7 14.0 21.0 28.0
Basket3 5 NaN NaN NaN
--------- Use of isnull() ---------
Apple Orange Banana Pear
Basket1 False False False False
Basket2 False False False False
Basket3 False True True True
--------- Use of notnull() ---------
Apple Orange Banana Pear
Basket1 True True True True
Basket2 True True True True
Basket3 True False False Falseimport pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [5,]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3'])
print("\n--------- DataFrame ---------\n")
print(df)
print("\n--------- Use of dropna() ---------\n")
print(df.dropna())--------- DataFrame ---------
Apple Orange Banana Pear
Basket1 10 20.0 30.0 40.0
Basket2 7 14.0 21.0 28.0
Basket3 5 NaN NaN NaN
--------- Use of dropna() ---------
Apple Orange Banana Pear
Basket1 10 20.0 30.0 40.0
Basket2 7 14.0 21.0 28.0import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [5,]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3'])
print("\n--------- DataFrame ---------\n")
print(df)
print("\n--------- Drop Columns) ---------\n")
print(df.dropna(1))--------- DataFrame ---------
Apple Orange Banana Pear
Basket1 10 20.0 30.0 40.0
Basket2 7 14.0 21.0 28.0
Basket3 5 NaN NaN NaN
--------- Drop Columns) ---------
Apple
Basket1 10
Basket2 7
Basket3 5import pandas as pd
df = pd.DataFrame({'DateOfBirth': ['1986-11-11', '1999-05-12', '1976-01-01',
'1986-06-01', '1983-06-04', '1990-03-07',
'1999-07-09'],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Pane', 'Aaron', 'Penelope', 'Frane',
'Christina', 'Cornelia'])
print(df.sort_index(ascending=False))DateOfBirth State
Penelope 1986-06-01 AL
Pane 1999-05-12 TX
Jane 1986-11-11 NY
Frane 1983-06-04 AK
Cornelia 1999-07-09 TX
Christina 1990-03-07 TX
Aaron 1976-01-01 FLimport pandas as pd
employees = pd.DataFrame({
'EmpCode': ['Emp001', 'Emp002', 'Emp003', 'Emp004', 'Emp005'],
'Name': ['John', 'Doe', 'William', 'Spark', 'Mark'],
'Occupation': ['Chemist', 'Statistician', 'Statistician',
'Statistician', 'Programmer'],
'Date Of Join': ['2018-01-25', '2018-01-26', '2018-01-26', '2018-02-26',
'2018-03-16'],
'Age': [23, 24, 34, 29, 40]})
print(employees.sort_index(axis=1, ascending=False))Occupation Name EmpCode Date Of Join Age
0 Chemist John Emp001 2018-01-25 23
1 Statistician Doe Emp002 2018-01-26 24
2 Statistician William Emp003 2018-01-26 34
3 Statistician Spark Emp004 2018-02-26 29
4 Programmer Mark Emp005 2018-03-16 40import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [5, 5, 0, 0]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3'])
print("\n--------- DataFrame Values--------\n")
print(df)
print("\n--------- DataFrame Values by Rank--------\n")
print(df.rank())--------- DataFrame Values--------
Apple Orange Banana Pear
Basket1 10 20 30 40
Basket2 7 14 21 28
Basket3 5 5 0 0
--------- DataFrame Values by Rank--------
Apple Orange Banana Pear
Basket1 3.0 3.0 3.0 3.0
Basket2 2.0 2.0 2.0 2.0
Basket3 1.0 1.0 1.0 1.0import pandas as pd
employees = pd.DataFrame({
'EmpCode': ['Emp001', 'Emp002', 'Emp003', 'Emp004', 'Emp005'],
'Name': ['John', 'Doe', 'William', 'Spark', 'Mark'],
'Occupation': ['Chemist', 'Statistician', 'Statistician',
'Statistician', 'Programmer'],
'Date Of Join': ['2018-01-25', '2018-01-26', '2018-01-26', '2018-02-26',
'2018-03-16'],
'Age': [23, 24, 34, 29, 40]})
print("\n --------- Before Index ----------- \n")
print(employees)
print("\n --------- Multiple Indexing ----------- \n")
print(employees.set_index(['Occupation', 'Age']))Date Of Join EmpCode Name
Occupation Age
Chemist 23 2018-01-25 Emp001 John
Statistician 24 2018-01-26 Emp002 Doe
34 2018-01-26 Emp003 William
29 2018-02-26 Emp004 Spark
Programmer 40 2018-03-16 Emp005 Markimport pandas as pd
values = ["India", "Canada", "Australia",
"Japan", "Germany", "France"]
pidx = pd.period_range('2015-01-01', periods=6)
df = pd.DataFrame(values, index=pidx, columns=['Country'])
print(df)Country
2015-01-01 India
2015-01-02 Canada
2015-01-03 Australia
2015-01-04 Japan
2015-01-05 Germany
2015-01-06 Franceimport pandas as pd
df = pd.read_csv('test.csv', index_col="DateTime")
print(df)Wheat Rice Oil
DateTime
10/10/2016 10.500 12.500 16.500
10/11/2016 11.250 12.750 17.150
10/12/2016 10.000 13.150 15.500
10/13/2016 12.000 14.500 16.100
10/14/2016 13.000 14.825 15.600
10/15/2016 13.075 15.465 15.315
10/16/2016 13.650 16.105 15.030
10/17/2016 14.225 16.745 14.745
10/18/2016 14.800 17.385 14.460
10/19/2016 15.375 18.025 14.175import pandas as pd
df = pd.DataFrame({'DateOfBirth': ['1986-11-11', '1999-05-12', '1976-01-01',
'1986-06-01', '1983-06-04', '1990-03-07',
'1999-07-09'],
'State': ['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
},
index=['Jane', 'Pane', 'Aaron', 'Penelope', 'Frane',
'Christina', 'Cornelia'])
df.to_csv('test.csv', encoding='utf-8', index=True)检查本地文件import pandas as pd
df = pd.read_csv("test.csv", usecols = ['Wheat','Oil'])
print(df)import pandas as pd
cols = list(pd.read_csv("test.csv", nrows =1))
print(cols)['DateTime', 'Wheat', 'Rice', 'Oil']import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3'])
print(df.ix[df['Apple'].idxmax()])Apple 55
Orange 15
Banana 8
Pear 12
Name: Basket3, dtype: int64import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3'])
print(df)
print("\n ----------- Filter data using query method ------------- \n")
df1 = df.ix[df.query('Apple > 50 & Orange <= 15 & Banana < 15 & Pear == 12').index]
print(df1)Apple Orange Banana Pear
Basket1 10 20 30 40
Basket2 7 14 21 28
Basket3 55 15 8 12
----------- Filter data using query method -------------
Apple Orange Banana Pear
Basket3 55 15 8 12import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3'])
if 'Apple' in df.columns:
print("Yes")
else:
print("No")
if set(['Apple','Orange']).issubset(df.columns):
print("Yes")
else:
print("No")import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12],
[15, 14, 1, 8], [7, 1, 1, 8], [5, 4, 9, 2]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n----------- nsmallest -----------\n")
print(df.nsmallest(2, ['Apple']))
print("\n----------- nlargest -----------\n")
print(df.nlargest(2, ['Apple']))----------- nsmallest -----------
Apple Orange Banana Pear
Basket6 5 4 9 2
Basket2 7 14 21 28
----------- nlargest -----------
Apple Orange Banana Pear
Basket3 55 15 8 12
Basket4 15 14 1 8import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12],
[15, 14, 1, 8], [7, 1, 1, 8], [5, 4, 9, 2]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n----------- Minimum -----------\n")
print(df[['Apple', 'Orange', 'Banana', 'Pear']].min())
print("\n----------- Maximum -----------\n")
print(df[['Apple', 'Orange', 'Banana', 'Pear']].max())----------- Minimum -----------
Apple 5
Orange 1
Banana 1
Pear 2
dtype: int64
----------- Maximum -----------
Apple 55
Orange 20
Banana 30
Pear 40
dtype: int64import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12],
[15, 14, 1, 8], [7, 1, 1, 8], [5, 4, 9, 2]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n----------- Minimum -----------\n")
print(df[['Apple', 'Orange', 'Banana', 'Pear']].idxmin())
print("\n----------- Maximum -----------\n")
print(df[['Apple', 'Orange', 'Banana', 'Pear']].idxmax())----------- Minimum -----------
Apple Basket6
Orange Basket5
Banana Basket4
Pear Basket6
dtype: object
----------- Maximum -----------
Apple Basket3
Orange Basket1
Banana Basket1
Pear Basket1
dtype: objectimport pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12],
[15, 14, 1, 8], [7, 1, 1, 8], [5, 4, 9, 2]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n----------- Cumulative Product -----------\n")
print(df[['Apple', 'Orange', 'Banana', 'Pear']].cumprod())
print("\n----------- Cumulative Sum -----------\n")
print(df[['Apple', 'Orange', 'Banana', 'Pear']].cumsum())----------- Cumulative Product -----------
Apple Orange Banana Pear
Basket1 10 20 30 40
Basket2 70 280 630 1120
Basket3 3850 4200 5040 13440
Basket4 57750 58800 5040 107520
Basket5 404250 58800 5040 860160
Basket6 2021250 235200 45360 1720320
----------- Cumulative Sum -----------
Apple Orange Banana Pear
Basket1 10 20 30 40
Basket2 17 34 51 68
Basket3 72 49 59 80
Basket4 87 63 60 88
Basket5 94 64 61 96
Basket6 99 68 70 98import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12],
[15, 14, 1, 8], [7, 1, 1, 8], [5, 4, 9, 2]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n----------- Describe DataFrame -----------\n")
print(df.describe())
print("\n----------- Describe Column -----------\n")
print(df[['Apple']].describe())----------- Describe DataFrame -----------
Apple Orange Banana Pear
count 6.000000 6.000000 6.000000 6.000000
mean 16.500000 11.333333 11.666667 16.333333
std 19.180719 7.257180 11.587349 14.555640
min 5.000000 1.000000 1.000000 2.000000
25% 7.000000 6.500000 2.750000 8.000000
50% 8.500000 14.000000 8.500000 10.000000
75% 13.750000 14.750000 18.000000 24.000000
max 55.000000 20.000000 30.000000 40.000000
----------- Describe Column -----------
Apple
count 6.000000
mean 16.500000
std 19.180719
min 5.000000
25% 7.000000
50% 8.500000
75% 13.750000
max 55.000000import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12],
[15, 14, 1, 8], [7, 1, 1, 8], [5, 4, 9, 2]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n----------- Calculate Mean -----------\n")
print(df.mean())
print("\n----------- Calculate Median -----------\n")
print(df.median())
print("\n----------- Calculate Mode -----------\n")
print(df.mode())----------- Calculate Mean -----------
Apple 16.500000
Orange 11.333333
Banana 11.666667
Pear 16.333333
dtype: float64
----------- Calculate Median -----------
Apple 8.5
Orange 14.0
Banana 8.5
Pear 10.0
dtype: float64
----------- Calculate Mode -----------
Apple Orange Banana Pear
0 7 14 1 8import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12],
[15, 14, 1, 8], [7, 1, 1, 8], [5, 4, 9, 2]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n----------- Calculate Mean -----------\n")
print(df.mean())
print("\n----------- Calculate Median -----------\n")
print(df.median())
print("\n----------- Calculate Mode -----------\n")
print(df.mode())----------- Measure Variance -----------
Apple 367.900000
Orange 52.666667
Banana 134.266667
Pear 211.866667
dtype: float64
----------- Standard Deviation -----------
Apple 19.180719
Orange 7.257180
Banana 11.587349
Pear 14.555640
dtype: float64import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12],
[15, 14, 1, 8], [7, 1, 1, 8], [5, 4, 9, 2]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n----------- Calculating Covariance -----------\n")
print(df.cov())
print("\n----------- Between 2 columns -----------\n")
# Covariance of Apple vs Orange
print(df.Apple.cov(df.Orange))----------- Calculating Covariance -----------
Apple Orange Banana Pear
Apple 367.9 47.600000 -40.200000 -35.000000
Orange 47.6 52.666667 54.333333 77.866667
Banana -40.2 54.333333 134.266667 154.933333
Pear -35.0 77.866667 154.933333 211.866667
----------- Between 2 columns -----------
47.60000000000001import pandas as pd
df1 = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12],
[15, 14, 1, 8], [7, 1, 1, 8], [5, 4, 9, 2]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n------ Calculating Correlation of one DataFrame Columns -----\n")
print(df1.corr())
df2 = pd.DataFrame([[52, 54, 58, 41], [14, 24, 51, 78], [55, 15, 8, 12],
[15, 14, 1, 8], [7, 17, 18, 98], [15, 34, 29, 52]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n----- Calculating correlation between two DataFrame -------\n")
print(df2.corrwith(other=df1))------ Calculating Correlation of one DataFrame Columns -----
Apple Orange Banana Pear
Apple 1.000000 0.341959 -0.180874 -0.125364
Orange 0.341959 1.000000 0.646122 0.737144
Banana -0.180874 0.646122 1.000000 0.918606
Pear -0.125364 0.737144 0.918606 1.000000
----- Calculating correlation between two DataFrame -------
Apple 0.678775
Orange 0.354993
Banana 0.920872
Pear 0.076919
dtype: float64import pandas as pd
df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12],
[15, 14, 1, 8], [7, 1, 1, 8], [5, 4, 9, 2]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n------ Percent change at each cell of a Column -----\n")
print(df[['Apple']].pct_change()[:3])
print("\n------ Percent change at each cell of a DataFrame -----\n")
print(df.pct_change()[:5])------ Percent change at each cell of a Column -----
Apple
Basket1 NaN
Basket2 -0.300000
Basket3 6.857143
------ Percent change at each cell of a DataFrame -----
Apple Orange Banana Pear
Basket1 NaN NaN NaN NaN
Basket2 -0.300000 -0.300000 -0.300000 -0.300000
Basket3 6.857143 0.071429 -0.619048 -0.571429
Basket4 -0.727273 -0.066667 -0.875000 -0.333333
Basket5 -0.533333 -0.928571 0.000000 0.000000import pandas as pd
df = pd.DataFrame([[10, 30, 40], [], [15, 8, 12],
[15, 14, 1, 8], [7, 8], [5, 4, 1]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n------ DataFrame with NaN -----\n")
print(df)
print("\n------ DataFrame with Forward Filling -----\n")
print(df.ffill())
print("\n------ DataFrame with Forward Filling -----\n")
print(df.bfill())------ DataFrame with NaN -----
Apple Orange Banana Pear
Basket1 10.0 30.0 40.0 NaN
Basket2 NaN NaN NaN NaN
Basket3 15.0 8.0 12.0 NaN
Basket4 15.0 14.0 1.0 8.0
Basket5 7.0 8.0 NaN NaN
Basket6 5.0 4.0 1.0 NaN
------ DataFrame with Forward Filling -----
Apple Orange Banana Pear
Basket1 10.0 30.0 40.0 NaN
Basket2 10.0 30.0 40.0 NaN
Basket3 15.0 8.0 12.0 NaN
Basket4 15.0 14.0 1.0 8.0
Basket5 7.0 8.0 1.0 8.0
Basket6 5.0 4.0 1.0 8.0
------ DataFrame with Forward Filling -----
Apple Orange Banana Pear
Basket1 10.0 30.0 40.0 8.0
Basket2 15.0 8.0 12.0 8.0
Basket3 15.0 8.0 12.0 8.0
Basket4 15.0 14.0 1.0 8.0
Basket5 7.0 8.0 1.0 NaN
Basket6 5.0 4.0 1.0 NaNimport pandas as pd
df = pd.DataFrame([[10, 30, 40], [], [15, 8, 12],
[15, 14, 1, 8], [7, 8], [5, 4, 1]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n------ DataFrame-----\n")
print(df)
print("\n------ Stacking DataFrame -----\n")
print(df.stack(level=-1))------ DataFrame-----
Apple Orange Banana Pear
Basket1 10.0 30.0 40.0 NaN
Basket2 NaN NaN NaN NaN
Basket3 15.0 8.0 12.0 NaN
Basket4 15.0 14.0 1.0 8.0
Basket5 7.0 8.0 NaN NaN
Basket6 5.0 4.0 1.0 NaN
------ Stacking DataFrame -----
Basket1 Apple 10.0
Orange 30.0
Banana 40.0
Basket3 Apple 15.0
Orange 8.0
Banana 12.0
Basket4 Apple 15.0
Orange 14.0
Banana 1.0
Pear 8.0
Basket5 Apple 7.0
Orange 8.0
Basket6 Apple 5.0
Orange 4.0
Banana 1.0
dtype: float64import pandas as pd
df = pd.DataFrame([[10, 30, 40], [], [15, 8, 12],
[15, 14, 1, 8], [7, 8], [5, 4, 1]],
columns=['Apple', 'Orange', 'Banana', 'Pear'],
index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
'Basket5', 'Basket6'])
print("\n------ DataFrame-----\n")
print(df)
print("\n------ Unstacking DataFrame -----\n")
print(df.unstack(level=-1))------ DataFrame-----
Apple Orange Banana Pear
Basket1 10.0 30.0 40.0 NaN
Basket2 NaN NaN NaN NaN
Basket3 15.0 8.0 12.0 NaN
Basket4 15.0 14.0 1.0 8.0
Basket5 7.0 8.0 NaN NaN
Basket6 5.0 4.0 1.0 NaN
------ Unstacking DataFrame -----
Apple Basket1 10.0
Basket2 NaN
Basket3 15.0
Basket4 15.0
Basket5 7.0
Basket6 5.0
Orange Basket1 30.0
Basket2 NaN
Basket3 8.0
Basket4 14.0
Basket5 8.0
Basket6 4.0
Banana Basket1 40.0
Basket2 NaN
Basket3 12.0
Basket4 1.0
Basket5 NaN
Basket6 1.0
Pear Basket1 NaN
Basket2 NaN
Basket3 NaN
Basket4 8.0
Basket5 NaN
Basket6 NaN
dtype: float64import pandas as pd
df pd.read_html("url")
版权声明：文章来自公众号(python风控模型),未经许可，不得抄袭。遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。我希望将Favorite模型添加到我的User和Link模型。业务逻辑用户可以有多个链接(即可以添加多个链接)用户可以收藏多个链接(他们自己的或其他用户的)一个链接可以被多个用户收藏，但只有一个所有者我对如何为这种关联建模以及在模型就位后如何创建用户收藏夹感到困惑？classUser 最佳答案 下面的数据模型怎么样:classUser:destroyhas_many:favorite_links,:through=>:favorites,:source=>:linkendclassLink:destroyhas_many:favor
本文主要介绍在使用Selenium进行自动化测试或者任务时，对于使用了iframe的页面，如何定位iframe中的元素文章目录场景描述解决方案具体代码场景描述当我们在使用Selenium进行自动化测试的时候，可能会遇到一些界面或者窗体是使用HTML的iframe标签进行承载的。对于iframe中的标签，如果直接查找是无法找到的，会抛出没有找到元素的异常。比如近在咫尺的例子就是，CSDN的登录窗体就是使用的iframe，大家可以尝试通过F12开发者模式查看到的tag_name,class_name,id或者xpath来定位中的页面元素，会抛出NoSuchElementException异常。解决
Region是HBase数据管理的基本单位,region有一点像关系型数据的分区。region中存储这用户的真实数据，而为了管理这些数据，HBase使用了RegionSever来管理region。Region的结构hbaseregion的大小设置默认情况下，每个Table起初只有一个Region，随着数据的不断写入，Region会自动进行拆分。刚拆分时，两个子Region都位于当前的RegionServer，但处于负载均衡的考虑，HMaster有可能会将某个Region转移给其他的RegionServer。RegionSplit时机：当1个region中的某个Store下所有StoreFile
我正在使用Maruku，将Markdown(超集)转换为HTML，你知道我该怎么做才能从HTML转换为Markdown吗？ 最佳答案 Google发现了一个名为reverse_markdown的ruby脚本.它似乎可以满足您的需求。 关于ruby-on-rails-我需要从HTML转到markdown，有什么建议吗？，我们在StackOverflow上找到一个类似的问题： https://stackoverflow.com/questions/175162
1．问题描述使用Python的turtle（海龟绘图）模块提供的函数绘制直线。2．问题分析一幅复杂的图形通常都可以由点、直线、三角形、矩形、平行四边形、圆、椭圆和圆弧等基本图形组成。其中的三角形、矩形、平行四边形又可以由直线组成，而直线又是由两个点确定的。我们使用Python的turtle模块所提供的函数来绘制直线。在使用之前我们先介绍一下turtle模块的相关知识点。turtle模块提供面向对象和面向过程两种形式的海龟绘图基本组件。面向对象的接口类如下：1）TurtleScreen类：定义图形窗口作为绘图海龟的运动场。它的构造器需要一个tkinter.Canvas或ScrolledCanva
我有一个带有Postgres数据库的Rails应用程序，该数据库有一个带有jsonbgenres列的Artists表。有几十万行。该行中的每个流派列都有一个类似["rock","indie","seenlive","alternative","indierock"]的数组，其中包含不同的流派。我想要做的是在所有行中以JSON格式输出每种类型的计数。类似于:{"rock":532,"powermetal":328,"indie":862}有没有办法有效地做到这一点？更新...这是我目前得到的...genres=Artist.all.pluck(:genres).flatten.delet
在Rails3中，当在MiniTest中编写功能测试时，我养成了将路由测试与Controller操作分开测试的习惯。我从RailsGuideonTesting-Section9:TestingRoutes得到了这个想法.然而，在将我的应用程序升级到Rails4之后，我注意到如果我不为get|patch|post|delete方法提供一组适当的参数。例如，给定路线:#config/routes.rbnamespace"api"donamespace"v2",defaults:{format::json}doresources:usersdoresources:postsdoresourc
有点边缘情况，但知道为什么&&=会这样吗？我正在使用1.9.2。obj=Object.newobj.instance_eval{@bar&&=@bar}#=>nil,expectedobj.instance_variables#=>[],soobjhasno@barinstancevariableobj.instance_eval{@bar=@bar&&@bar}#ostensiblythesameas@bar&&=@barobj.instance_variables#=>[:@bar]#whywouldthisversioninitialize@bar?为了比较，||=将实例变量初始
假设我们有这样的东西:classCompanyincludeMongoid::Documenthas_many:usersfield:name,type:StringendclassUserincludeMongoid::Documentbelongs_to:companyfield:name,type:StringendmoduleCompanyRepresenterincludeRoar::Representer::JSONproperty:nameendmoduleUserRepresenterincludeRoar::Representer::JSONproperty:name
谈到现状，国内的软件测试行情目前呈现了两极分化的极端情况。一个是早期的手工测试人员吐槽工作不好做，即使有工作也是外包，而且薪资太低；一方面是很多互联网企业感叹自动化测试人才难找，有技术的自动化测试工程师，高薪难聘。这两者其实并不矛盾。手工测试工作难找也确实是目前真实的行情早期从事功能测试的手工测试人员，在测试方面大多采用手动、人工执行的方式查找软件缺陷和BUG，用行业术语来描述就是“点点点”。这种测试方式耗费大量人力和资源，工作效率却十分低下。在早期软件复杂和迭代程度不高的情况下，有资本的企业会“供养”一批这样的手工测试人员。但对测试员本身来讲，毫无技术难度的工作，和几乎没有保障的薪资水平，直