小编use*_*113的帖子

我正在尝试对CSV文件中的数据执行PCA分析,但是当我尝试扩展数据时,我不断收到一个奇怪的警告.

def prepare_data(filename):
    df=pd.read_csv(filename,index_col=0)
    df.dropna(axis=0,how='any',inplace=True)
    return df

def perform_PCA(df):
    threshold = 0.3
    component = 1 #Second of two right now
    pca = decomposition.PCA(n_components=2)
    print df.head()
    scaled_data = preprocessing.scale(df)
    #pca.fit(scaled_data)
    #transformed = pca.transform(scaled_data)
    #pca_components_df = pd.DataFrame(data = pca.components_,columns = df.columns.values)

这是我不断得到的警告.

C:\Users\mbellissimo\AppData\Local\Continuum\Anaconda\lib\site-packages\sklearn\utils\validation.py:498: UserWarning: The scale function assumes floating point values as input, got int64
  "got %s" % (estimator, X.dtype))
C:\Users\mbellissimo\AppData\Local\Continuum\Anaconda\lib\site-packages\sklearn\preprocessing\data.py:145: DeprecationWarning: Implicitly casting between incompatible kinds. In a future numpy release, this will raise an error. Use casting="unsafe" if this is …