def Sta_inf(data): print('_min',np.min(data)) print('_max:',np.max(data)) print('_mean',np.mean(data)) print('_ptp',np.ptp(data)) print('_std',np.std(data)) print('_var',np.var(data)) 1 print('Sta of label:') Sta_inf(Y_data)改为pyspark语句

Assuming that the data is stored in a PySpark DataFrame called "df" and the label column is called "label":

from pyspark.sql.functions import min, max, mean, stddev, varience

# Calculate statistics of data
df.select([min(col), max(col), mean(col), stddev(col), varience(col)]).show()

# Calculate statistics of label
df.select([min('label'), max('label'), mean('label'), stddev('label'), varience('label')]).show()

Note that the "col" variable should be replaced with the name of the column containing the data you want to analyze.

相关问题

print('Sta of label:') Sta_inf(Y_data)改为pyspark语句

假设Y_data是一个PySpark DataFrame，您可以使用以下代码：

from pyspark.sql.functions import col

print('Sta of label:')
Y_data.select([col(c).alias("Sta of label") for c in Y_data.columns]).describe().show()

这将输出Y_data DataFrame中每个列的统计信息。请注意，您需要在代码中导入pyspark的相关功能。