[英]Insert a series of values into pd.dataframe randomly
I have a large dataframe and what I want to do is overwrite X entries of that dataframe with a new value I set.我有一个大数据框,我想要做的是用我设置的新值覆盖该数据框的 X 个条目。 The new entries have to be at a random position, but they have to be in order.
新条目必须位于随机位置,但必须按顺序排列。 Like I have a Column with random numbers, and want to overwrite 20 of them in a row with the new value x.
就像我有一个带有随机数的列,并且想用新值 x 连续覆盖其中的 20 个。
I tried df.sample(x)
and then update the dataframe, but I only get individual entries.我试过
df.sample(x)
然后更新数据df.sample(x)
,但我只得到单个条目。 But I need the X new entries in a row (consecutively).但是我需要连续的 X 个新条目(连续)。
Somebody got a solution?有人有解决方案吗? I'm quite new to Python and have to get into it for my master thesis.
我对 Python 很陌生,必须在我的硕士论文中学习它。
CLARIFICATION:澄清:
My dataframe has 5 columns with almost 60,000 rows, each row for 10 minutes of the year.我的数据框有 5 列,几乎有 60,000 行,每行一年中的 10 分钟。
I tried:我试过:
df_update = df.sample(12)
df_update.status = 'reduced'
df.update(df_update)
df.loc[('status) == 'reduced', ['production']] *=0.6
which does the trick for the total amount of time (12*10 minutes), but I want 120 consecutive minutes and not separated.这对总时间(12 * 10 分钟)有效,但我想要连续 120 分钟而不是分开。
I decided to get a random value and just index the next 12 entries to be 0.6.我决定获取一个随机值并将接下来的 12 个条目索引为 0.6。 I think this is what you want.
我想这就是你想要的。
df = pd.DataFrame({'output':np.random.randn(20),'status':[0]*20})
idx = df.sample(1).index.values[0]
df.loc[idx:idx+11,"output"]=0.6
df.loc[idx:idx+11,"status"]=1
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