python-2.7

问题 I have a csv file in the format IDATE_TIMESTAMP,OPEN,HIGH,LOW,CLOSE,VOLUME 1535535060,94.36,94.36,94.36,94.36,1 1535535120,94.36,94.36,93.8,93.8,1 1535535180,93.8,93.8,93.8,93.8,0 1535535240,93.8,93.8,93.74,93.74,1 1535535300,93.74,93.74,93.74,93.74,0 1535535360,93.74,93.74,93.74,93.74,0 1535535420,93.74,93.74,93.74,93.74,0 1535535480,93.74,93.74,93.74,93.74,0 1535535540,93.74,93.74,93.74,93.74,0 . . . . I have to and from timestamp which will filter out the data from the file and return the

问题 I want to write a python code that downloads 'main' image from urls that contain images. I have urls like these in my data (text files) http://t.co/fd9F0Gp1P1 points to an fb image http://t.co/0Ldy6j26fb points to twitter image but their expanded urls don't result in .jpg,.png images. Instead they direct us to a page that contains the desired image. How do I download images from these urls? 回答1: Here you will find an example of how I downloaded the plane image from the facebook page, you can

问题 I want to write a python code that downloads 'main' image from urls that contain images. I have urls like these in my data (text files) http://t.co/fd9F0Gp1P1 points to an fb image http://t.co/0Ldy6j26fb points to twitter image but their expanded urls don't result in .jpg,.png images. Instead they direct us to a page that contains the desired image. How do I download images from these urls? 回答1: Here you will find an example of how I downloaded the plane image from the facebook page, you can

问题 I have 12 unique groups that I am trying to randomly sample from, each with a different number of observations. I want to randomly sample from the entire population (dataframe) with each group having the same probability of being selected from. The simplest example of this would be a dataframe with 2 groups. groups probability 0 a 0.25 1 a 0.25 2 b 0.5 using np.random.choice(df['groups'], p=df['probability'], size=100) Each iteration will now have a 50% chance of selecting group a and a 50%

问题 I have 12 unique groups that I am trying to randomly sample from, each with a different number of observations. I want to randomly sample from the entire population (dataframe) with each group having the same probability of being selected from. The simplest example of this would be a dataframe with 2 groups. groups probability 0 a 0.25 1 a 0.25 2 b 0.5 using np.random.choice(df['groups'], p=df['probability'], size=100) Each iteration will now have a 50% chance of selecting group a and a 50%

问题 I need a bit of help to understand how python initialising works. I have a class (Bar) with another class (Foo) as a field/variable. When I try to initialise this variable directly in Bar (not in the class __init__) all instances of Bar will point to the same Foo. But if I have an __init__ method, as in Bar2, each Bar2 instance will have a unique Foo instance. What is happening here? class Foo(): number = 0 class Bar(): foo = Foo() class Bar2(): foo = None def __init__(self): self.foo = Foo()

问题 I need a bit of help to understand how python initialising works. I have a class (Bar) with another class (Foo) as a field/variable. When I try to initialise this variable directly in Bar (not in the class __init__) all instances of Bar will point to the same Foo. But if I have an __init__ method, as in Bar2, each Bar2 instance will have a unique Foo instance. What is happening here? class Foo(): number = 0 class Bar(): foo = Foo() class Bar2(): foo = None def __init__(self): self.foo = Foo()

问题 I need a bit of help to understand how python initialising works. I have a class (Bar) with another class (Foo) as a field/variable. When I try to initialise this variable directly in Bar (not in the class __init__) all instances of Bar will point to the same Foo. But if I have an __init__ method, as in Bar2, each Bar2 instance will have a unique Foo instance. What is happening here? class Foo(): number = 0 class Bar(): foo = Foo() class Bar2(): foo = None def __init__(self): self.foo = Foo()

问题 I need a bit of help to understand how python initialising works. I have a class (Bar) with another class (Foo) as a field/variable. When I try to initialise this variable directly in Bar (not in the class __init__) all instances of Bar will point to the same Foo. But if I have an __init__ method, as in Bar2, each Bar2 instance will have a unique Foo instance. What is happening here? class Foo(): number = 0 class Bar(): foo = Foo() class Bar2(): foo = None def __init__(self): self.foo = Foo()

问题 This question already has answers here : Actual meaning of 'shell=True' in subprocess (6 answers) Closed 4 years ago . I just started working with python subprocess module. The code subprocess.call("ls", shell = False) and subprocess.call("ls", shell = True) both return the same results. I just want to know what is the main difference between the two shell options. 回答1: Straight out of the Docs: If shell is True, the specified command will be executed through the shell. This can be useful if