Why do these two commands yield different results?

I have a numpy array

x_pcaed

of shape (2250,2) and a pandas dataframe

seeds_train

with 2250 rows, which has a column

y

whose values are 0 or 1.
I made x_pcaed into a dataframe as follows:

pca_df = pd.DataFrame(data = x_pcaed, columns=['pr_comp_1', 'pr_comp_2'])

I then created a new dataframe as follows:

test1 = pd.concat([pca_df, seeds_train[['y']]], axis=1)

I'm confused why the following two commands yield different outputs:

test1.loc[test1['y']==0,'pr_comp_1'].values

x_pcaed[seeds_train.y==0, 0]

My understanding is that they should give the same result, so I must be missing something.

Update: Here's the full code. It uses Pumpkin Seeds Dataset from here.

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler

seeds = pd.read_excel('Pumpkin_Seeds_Dataset.xlsx')
seeds['y'] = 0
seeds.loc[seeds.Class=='Ürgüp Sivrisi', 'y']=1

seeds_train, seeds_test = train_test_split(seeds.copy(),
                                              shuffle=True,
                                              random_state=123,
                                              test_size=.1,
                                              stratify=seeds.y.values)

features = seeds_train.columns[:-2]
x = seeds_train.loc[:,features].values
scaler = StandardScaler()
x_scaled = scaler.fit_transform(x)
pca = PCA(n_components = 2)
x_pcaed = pca.fit_transform(x_scaled)
pca_df = pd.DataFrame(data = x_pcaed, columns=['pr_comp_1', 'pr_comp_2'])
test1 = pd.concat([pca_df, seeds_train[['y']]], axis=1)

print(test1.loc[test1['y']==0,'pr_comp_1'].values)
print('----------')
print(x_pcaed[seeds_train.y==0, 0])

Output:

[-0.79984874 -2.75176272 -0.26329661 ... -2.03461928 -2.38149466
 -1.46663563]
----------
[-1.36392527 -0.26329661 -4.91873745 ... -1.46508442 -1.07096868
 -4.79462993]

Update

You forgot the index when you create pca_df so the indexes are not aligned between pca_df and seed_train when you use pd.concat along index axis. Use:

pca_df = pd.DataFrame(data=x_pcaed,
                      columns=['pr_comp_1', 'pr_comp_2'], 
                      index=seeds_train.index)

test1 = pd.concat([pca_df, seeds_train[['y']]], axis=1)

r1 = test1.loc[test1['y']==0, 'pr_comp_1'].values
r2 = x_pcaed[seeds_train.y==0, 0]
print(r1)
print(r2)

# Output
array([-1.36392527, -0.26329661, -4.91873745, ..., -1.46508442,
       -1.07096868, -4.79462993])
array([-1.36392527, -0.26329661, -4.91873745, ..., -1.46508442,
       -1.07096868, -4.79462993])

There is no problem with your code:

rng =  np.random.default_rng(seed=2023)

x_pcaed = rng.random((2250, 2))
seeds_train = pd.DataFrame({'y': rng.choice([0, 1], 2250)})

pca_df = pd.DataFrame(data=x_pcaed, columns=['pr_comp_1', 'pr_comp_2'])
test1 = pd.concat([pca_df, seeds_train[['y']]], axis=1)

r1 = test1.loc[test1['y']==0, 'pr_comp_1'].values
r2 = x_pcaed[seeds_train.y==0, 0]

Test:

>>> np.all(r1 == r2)
True

Note: take care after creating pca_df, if you modify x_pcaed, your dataframe will be modified too. Your numpy array is not copied to the dataframe but linked. It's different for test dataframe because pd.concat returns a copy so the data are detached from its reference.