Save a pandas dataframe containing numpy arrays-CodePudding

I have a dataframe with a column full of numpy arrays.

    A     B         C
0   1.0   0.000000  [[0. 1.],[0. 1.]]
1   2.0   0.000000  [[85. 1.],[52. 0.]]
2   3.0   0.000000  [[5. 1.],[0. 0.]]
3   1.0   3.333333  [[0. 1.],[41. 0.]]
4   2.0   3.333333  [[85. 1.],[0. 21.]]

Problem is, when I save it as a CSV file, and when i load it on another python file, the numpy column is read as text.

I tried to transform the column with np.fromstring() or np.loadtxt() but it doesn't work.

Example of and array after pd.read_csv()

"[[ 85.  1.]\n [   52.            0.        ]]"

Thanks

CodePudding user response：

The code below should work. I used another question to solve it, theres a bit more explanation in there: Convert a string with brackets to numpy array

import pandas as pd
import numpy as np

from ast import literal_eval

# Recreating DataFrame
data = np.array([0, 1, 0, 1, 85, 1, 52, 0, 5, 1, 0, 0, 0, 1, 41, 0, 85, 1, 0, 21], dtype='float')
data = data.reshape((5,2,2))

write_df = pd.DataFrame({'A': [1.0,2.0,3.0,1.0,2.0],
                   'B': [0,0,0,3 1/3,3 1/3],
                   'C': data.tolist()})

# Saving DataFrame to CSV
fpath = 'D:\\Data\\test.csv'
write_df.to_csv(fpath)

# Reading DataFrame from CSV
read_df = pd.read_csv(fpath)

# literal_eval converts the string to a list of tuples
# np.array can convert this list of tuples directly into an array
def makeArray(rawdata):
    string = literal_eval(rawdata)
    return np.array(string)

# Applying the function row-wise, there could be a more efficient way
read_df['C'] = read_df['C'].apply(lambda x: makeArray(x))

CodePudding user response：

You can try .to_json()

output = pd.DataFrame([
  {'a':1,'b':np.arange(4)},
  {'a':2,'b':np.arange(5)}
]).to_json()

But you will get only lists back when reloading with

df=pd.read_json()

Turn them to numpy arrays with:

df['b']=[np.array(v) for v in df['b']]

CodePudding user response：

Here is an ugly solution.

import pandas as pd
import numpy as np

### Create dataframe
a = [1.0, 2.0, 3.0, 1.0, 2.0]
b = [0.000000,0.000000,0.000000,3.333333,3.333333]
c = [np.array([[0. ,1.],[0. ,1.]]),
np.array([[85. ,1.2],[52. ,0.]]),
np.array([[5. ,1.],[0. ,0.]]),
np.array([[0. ,1.],[41. ,0.]]),
np.array([[85. ,1.],[0. ,21.]]),]


df = pd.DataFrame({"a":a,"b":b,"c":c})

#### Save to csv

df.to_csv("to_trash.csv")
df = pd.read_csv("to_trash.csv")

### Bad string manipulation that could be done better with regex

df["c"] = ("np.array(" (df
 .c
 .str.split()
 .str.join(' ')
 .str.replace(" ",",")
 .str.replace(",,",",")
 .str.replace("[,", "[", regex=False)
) ")").apply(lambda x: eval(x))