英文:
How to create a sequence of rows of Data Frame based on starting and ending value defined by columns
问题
我有以下的数据框：
example_df = pd.DataFrame({'id': {0: 0, 1: 1, 2: 2, 3: 3, 4: 4},'seq_start': {0: 0.0, 1: 2800.0, 2: 6400.0, 3: 8400.0, 4: 9800.0},'seq_end': {0: 1400.0, 1: 4700.0, 2: 8400.0, 3: 9800.0, 4: 11400.0}})
我想要获得一个数据框，其中包含从 example_df['seq_start'] 到 example_df['seq_end'] 的值序列，以便稍后在连接中使用新创建的列。
所以期望的输出如下：
out_df = pd.DataFrame({'id': np.concatenate([[0] * 15, [1] * 20, [2] * 21]),'expected_output': np.concatenate([np.arange(0, 1500, 100),np.arange(2800, 4800, 100),np.arange(6400, 8500, 100)])})
如何处理这个问题？
英文:
I've got a following Data Frame:
example_df = pd.DataFrame({'id': {0: 0, 1: 1, 2: 2, 3: 3, 4: 4},'seq_start': {0: 0.0, 1: 2800.0, 2: 6400.0, 3: 8400.0, 4: 9800.0},'seq_end': {0: 1400.0, 1: 4700.0, 2: 8400.0, 3: 9800.0, 4: 11400.0}})
I'd like to obtain a Data Frame that has a sequences of values from example_df['seq_start'] to example_df['seq_end'] so that I could later use newly created column in a join.
So the expected output would look like below:
out_df = pd.DataFrame({'id': np.concatenate([[0] * 15, [1] * 20, [2] * 21]),'expected_output': np.concatenate([np.arange(0, 1500, 100),np.arange(2800, 4800, 100),np.arange(6400, 8500, 100)])})id expected_output0 0 01 0 1002 0 2003 0 3004 0 4005 0 500...12 0 120013 0 130014 0 140015 1 280016 1 290017 1 3000...31 1 440032 1 450033 1 460034 1 470035 2 640036 2 650037 2 6600...54 2 830055 2 8400
How can I approach this?
答案1
得分: 2
使用 pandas.DataFrame.explode：
def listify(x, step=100, right_closed=True):lower, upper = sorted(x)return range(lower, upper+step*right_closed, step)example_df['expected'] = example_df[['seq_end', 'seq_start']].astype(int).apply(listify, 1)new_df = example_df[['id','expected']].explode('expected')print(new_df)
输出：
id expected0 0 00 0 1000 0 2000 0 3000 0 400...4 4 110004 4 111004 4 112004 4 113004 4 11400
英文:
Using pandas.DataFrame.explode:
def listify(x, step=100, right_closed=True):lower, upper = sorted(x)return range(lower, upper+step*right_closed, step)example_df['expected'] = example_df[['seq_end', 'seq_start']].astype(int).apply(listify, 1)new_df = example_df[['id','expected']].explode('expected')print(new_df)
Output:
id expected0 0 00 0 1000 0 2000 0 3000 0 400.. .. ...4 4 110004 4 111004 4 112004 4 113004 4 11400
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