两个非连续行的差异 - Pandas
[英]Difference of two non-consecutive rows - Pandas
问题描述
I have a historical price list and I want to calculate the variation between prices for each currency.
我有一个历史价格表，我想计算每种货币价格之间的差异。 My code will update the list by getting the new price and append it to the database.我的代码将通过获取新价格和 append 将其更新到数据库来更新列表。 How can I do it?我该怎么做？ This is how the elements are on the table:这是元素在桌子上的样子：
Date Hour Currency Price Variation
0 2021-05-01 23:19:21 BAT 1.0700
1 2021-05-01 23:19:21 BTC 47922.1400
2 2021-05-01 23:19:21 DOGE 0.3286
3 2021-05-01 23:19:21 ETH 2451.7400
4 2021-05-01 23:35:50 BAT 1.0600
5 2021-05-01 23:35:50 BTC 47557.2700
6 2021-05-01 23:35:50 DOGE 0.3228
7 2021-05-01 23:35:50 ETH 2438.0300
8 2021-05-01 23:37:20 BAT 1.0500
9 2021-05-01 23:37:20 BTC 47467.0200
10 2021-05-01 23:37:20 DOGE 0.3209
11 2021-05-01 23:37:20 ETH 2435.3000
So, as you can see, the currencies are not consecutively placed.因此，如您所见，货币不是连续放置的。 For example:例如：
The price variation of BAT: BAT的价格变化：
0 -> 4 : (1.0600-1.0700)/1.0700 = -0.93%
4 -> 8 : (1.0500-1.0600)/1.0600 = -0.94%
last_value_index -> recent_value_index : (recent_value-last_value)/last_value
Thanks!谢谢！
1 个解决方案
解决方案1
0 已采纳 2021-05-02 03:51:24
We can group by Currency and then apply pct_change() on Price column我们可以按Currency分组，然后在Price列上应用pct_change()
df['Variation'] = 100*df.groupby('Currency').Price.pct_change()
OR Calculating the percentage change manually或手动计算百分比变化
df['Variation'] = df.groupby('Currency').Price.transform(lambda x: 100*x.diff()/x)
Output for newly provided df Output 用于新提供的 df
Date Hour Currency Price Variation
0 2021-05-01 23:19:21 BAT 1.0700 NaN
1 2021-05-01 23:19:21 BTC 47922.1400 NaN
2 2021-05-01 23:19:21 DOGE 0.3286 NaN
3 2021-05-01 23:19:21 ETH 2451.7400 NaN
4 2021-05-01 23:35:50 BAT 1.0600 -0.934579
5 2021-05-01 23:35:50 BTC 47557.2700 -0.761381
6 2021-05-01 23:35:50 DOGE 0.3228 -1.765064
7 2021-05-01 23:35:50 ETH 2438.0300 -0.559195
8 2021-05-01 23:37:20 BAT 1.0500 -0.943396
9 2021-05-01 23:37:20 BTC 47467.0200 -0.189771
10 2021-05-01 23:37:20 DOGE 0.3209 -0.588600
11 2021-05-01 23:37:20 ETH 2435.3000 -0.111976
12 2021-05-02 00:04:40 BAT 1.0200 -2.857143
13 2021-05-02 00:04:40 BTC 46883.6300 -1.229043
14 2021-05-02 00:04:40 DOGE 0.3028 -5.640386
15 2021-05-02 00:04:40 ETH 2397.8200 -1.539030
If we want to fillna with any values like 0.0.如果我们想用 0.0 之类的任何值填充。
df['Variation'] = 100*df.groupby('Currency').Price.pct_change().fillna(0.)
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