如何在 pandas dataframe 上迭代以在每次迭代中获取多行

Question

I have a pandas Dataframe.

DF.shape = (13096,27)

I want to iterate on the dataframe and for each iteration I take a shape of (50, 25) . I mean by 25, the first 25 columns.

I did it using the following code:

for i in test_df.iterrows():
        df1 = test_df.iloc[:50, 0:25]
        df1 = np.array(df1)

        seq_test_array = df1[newaxis, :, :]
        print('df1', seq_test_array.shape)

        #a = np.arange(10)
        #for i in np.nditer(seq_test_array):
        predictions = model.predict_classes(seq_test_array,verbose=1, batch_size=50)
        fig_verify = plt.figure(figsize=(5, 5))
        plt.plot(predictions, color="blue")
        plt.plot(predictions, color="green")
        plt.title('prediction')
        plt.ylabel('value')
        plt.xlabel('row')
        plt.show()

        print('predictions', predictions)
        preds = model.predict(seq_test_array)
        print('preds', preds)
        prediction = np.argmax(preds)
        print('prediction', prediction)

I dislayed the figure but they are empty. And the predictions, pred values the same (results of the print):

predictions [[1]]
preds [[0.9416911]]
prediction 0
df1 (1, 50, 25)

Is because my code fault?

Could you please help me? Thanks

Answer 1

You could try something like this:

sliced=50  
for i in range(0,len(df)-(sliced-1),sliced):
    subdf=df.iloc[i:i+sliced,df.columns[:-2]]
    ....
    #the rest of your code 

---

So, for example:

import numpy as np
import pandas as pd


N_rows=6
N_cols=5
df = pd.DataFrame(np.zeros((N_rows, N_cols)))
print(df)

sliced=2
for i in range(0,len(df)-(sliced-1),sliced):
    subdf=df.iloc[i:i+sliced,df.columns[:-2]]
    print(subdf)
    print(subdf.shape)

Output:

df
     0    1    2    3    4
0  0.0  0.0  0.0  0.0  0.0
1  0.0  0.0  0.0  0.0  0.0
2  0.0  0.0  0.0  0.0  0.0
3  0.0  0.0  0.0  0.0  0.0
4  0.0  0.0  0.0  0.0  0.0
5  0.0  0.0  0.0  0.0  0.0


Iterations:

     0    1    2
0  0.0  0.0  0.0
1  0.0  0.0  0.0
(2, 3)
     0    1    2
2  0.0  0.0  0.0
3  0.0  0.0  0.0
(2, 3)
     0    1    2
4  0.0  0.0  0.0
5  0.0  0.0  0.0
(2, 3)

So, as you can see, each iteration it takes a shape of (2,3) it means (sliced, len(df.columns)-2) , so in your case it will be (50, 25) .