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How can I use Pandas or Numpy to infer a datatype from a list of values?


How can I use Pandas or Numpy to infer a datatype from a list of values?

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How can I use Pandas or Numpy to infer a datatype from a list of values?
Tag : python , By : socurious
Date : November 24 2020, 09:00 AM

it should still fix some issue Because you have a mixed dtype initially even after calling dropna then you can coerce the dtype, seeing as all you're interested in is preserving numeric and bool types then calling convert_objects or to_numeric will correctly convert the dtype:
In [31]:
pd.Series([True,False,True,None]).dropna().convert_objects(convert_numeric=True)

Out[31]:
0     True
1    False
2     True
dtype: bool

In [30]:    
pd.to_numeric(pd.Series([True,False,True,None]).dropna())

Out[30]:
0     True
1    False
2     True
dtype: bool

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Efficient Datatype Python (list or numpy array?)


Tag : python , By : Juice
Date : March 29 2020, 07:55 AM
around this issue As I see it, if you were doing this in C or Fortran, you'd have to have an idea of the size of the array so that you can allocate the correct amount of memory (ignoring realloc!). So assuming you do know this, why do you need to append to the array?
In any case, numpy arrays have the resize method, which you can use to extend the size of the array.

Why in this architecture 'Map' function can't infer the map datatype?


Tag : chash , By : Murali Ravipudi
Date : March 29 2020, 07:55 AM
Hope this helps You can't infer types by return types, however a slight change in architecture lets you not need to do it, you need to make the base class generic.
public abstract class Repository<TEntity> : IRepository 
    where TEntity : Repository<TEntity>, new()
{
    public TEntity Map()
    {
        TEntity clone = new TEntity();
        // (Snip)
        return clone;
    }
}
public class Usuario : Repository
public class Usuario : Repository<Usuario>
[ResponseType(typeof(Usuario))]
public IHttpActionResult GetUsuario(int id)
{
    Usuario usuario = db.Usuarios.Find(id);
    if (usuario == null)
    {
        return NotFound();
    }

    return Ok(usuario.Map());
}

numpy/pandas: how to add a new column with values based on a list of indices?


Tag : python , By : TRobison
Date : March 29 2020, 07:55 AM
like below fixes the issue For example, I have a long list of indices: {1,3,7,9,...}. , Assuming col1 and col2 are in a Pandas DataFrame called df...
selected_indices = [1,3,7,9]   

# set index as col1, since that seems to be the point of column1
df.set_index('col1')

# define col3 value as 0 or 1 based on selected_indices list
df['col3'] = 0
df['col3'].loc[selected_indices] = 1

Pandas: Create a dataframe from a list of dictionaries where the values are all numpy arrays?


Tag : python , By : BinaryBoy
Date : March 29 2020, 07:55 AM
Any of those help I have list of dictionaries with the values are all np arrays. , Use pd.concat.
import numpy as np
import pandas as pd

datas = [
    {'begin_bal': np.arange(100), 'prin': np.arange(100)},
    {'begin_bal': np.arange(50), 'prin': np.arange(50)},
    {'begin_bal': np.arange(360), 'prin': np.arange(360)},
]

pd.concat(pd.DataFrame(data) for data in datas)

How do I load a tab-delimited file of mixed datatype into numpy or pandas?


Tag : python , By : kangfoo2
Date : March 29 2020, 07:55 AM
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