spark=SparkSession.builder.appName("local").enableHiveSupport().getOrCreate() pdf=pd.DataFrame(np.arange(20).reshape(4,5),columns=["a","b","c","d","e"]) df=spark.createDataFrame(pdf) df.agg(fn.count("a").alias("a_count"),fn.countDistinct(df.b),fn.sum("c"),fn.max("d"...
df.withColumnRenamed("OldColName","NewColName").columns 1. 2. 注: withColum一个投机取巧的用处,就是用来重命名列。(我不建议这么用,更推荐使用其对应的专门方法。尤其在跟别人合作工程的时候,使用专门的方法交接和沟通更有效。不要使用不常规的方法来炫技,增加大家工作的负担。)而且,这个方法是通过新增一列...
增加一列,用df['新列名'] = 新列值的形式,在原数据基础上赋值即可 df=pd.DataFrame(np.random.randn(6,4),columns=list('ABCD'))print(df)df['新增的列']=range(1,len(df)+1)df['新增的列2']=['abc','bc','cd','addc','dd','efsgs']print(df.head())print(len(df))#表示数据集有...
Well organized and easy to understand Web building tutorials with lots of examples of how to use HTML, CSS, JavaScript, SQL, Python, PHP, Bootstrap, Java, XML and more.
In Python, the numbering of rows starts with zero.Now, we can use Python to count the columns and rows.We can use df.shape[1] to find the number of columns:Example Count the number of columns: count_column = df.shape[1]print(count_column) Try it Yourself » ...
pandas.DataFrame( data, index, columns, dtype, copy) 一、创建DataFrame Pandas数据帧(DataFrame)可以使用各种输入创建,如 - 列表 字典 系列 Numpy ndarrays 另一个数据帧(DataFrame) 在本章的后续章节中,我们将看到如何使用这些输入创建数据帧(DataFrame)。
DataFrame.query(expr[, inplace])Query the columns of a frame with a boolean expression. 二元运算 方法描述 DataFrame.add(other[, axis, level, fill_value])加法,元素指向 DataFrame.sub(other[, axis, level, fill_value])减法,元素指向 DataFrame.mul(other[, axis, level, fill_value])乘法,元素指...
Query the columns of a frame with a boolean expression. 二元运算 方法描述DataFrame.add(other[, axis, level, fill_value])加法,元素指向DataFrame.sub(other[, axis, level, fill_value])减法,元素指向DataFrame.mul(other[, axis, level, fill_value])乘法,元素指向DataFrame.div(other[, axis, level,...
DataFrame, apply_func: callable, window: int, return_col_num: int, **kwargs): """ rolling with multiple columns on 2 dim pd.Dataframe * the result can apply the function which can return pd.Series with multiple columns call apply function with numpy ndarray :param return_col_num: 返回...
DataFrame(names,columns=['First_name']) df['name_match'] = df['First_name'].apply(lambda x: 'Match' if x == 'Bill' else 'Mismatch') print (df) 查询结果如下: (5) IF condition with OR 最后的案例中,我们尝试实现下面的 IF 条件: 当name是Bill或者Emma时,填值 Match 当name既不是...