CODEWITHSUNDEEP
pythonpandasdataframe
nikisaku
nikisaku

Reputation: 45

Finding longest consecutive increase in Pandas

I have a dataframe:

Date    Price
2021-01-01 29344.67
2021-01-02 32072.08
2021-01-03 33048.03
2021-01-04 32084.61
2021-01-05 34105.46
2021-01-06 36910.18
2021-01-07 39505.51
2021-01-08 40809.93
2021-01-09 40397.52
2021-01-10 38505.49

Date      object
Price    float64
dtype: object

And my goal is to find the longest consecutive period of growth. It should return: Longest consecutive period was from 2021-01-04 to 2021-01-08 with increase of $8725.32 and honestly I have no idea where to start with it. These are my first steps in pandas and I don't know which tools I should use to get this information.

Could anyone help me / point me in the right direction?

Upvotes: 3

Views: 909

Answers (2)

BENY
BENY

Reputation: 323226

Something like what Quang did for split the group , then pick the number of group

s = df.Price.diff().lt(0).cumsum()
out = df.loc[s==s.value_counts().sort_values().index[-1]]
Out[514]: 
         Date     Price
3  2021-01-04  32084.61
4  2021-01-05  34105.46
5  2021-01-06  36910.18
6  2021-01-07  39505.51
7  2021-01-08  40809.93

Upvotes: 0

Quang Hoang
Quang Hoang

Reputation: 150735

Detect your increasing sequence with cumsum on decreasing:

df['is_increasing'] = df['Price'].diff().lt(0).cumsum()

You would get:

         Date     Price  is_increasing
0  2021-01-01  29344.67             0
1  2021-01-02  32072.08             0
2  2021-01-03  33048.03             0
3  2021-01-04  32084.61             1
4  2021-01-05  34105.46             1
5  2021-01-06  36910.18             1
6  2021-01-07  39505.51             1
7  2021-01-08  40809.93             1
8  2021-01-09  40397.52             2
9  2021-01-10  38505.49             3

Now, you can detect your longest sequence with

sizes=df.groupby('is_increasing')['Price'].transform('size')
df[sizes == sizes.max()]

And you get:

         Date     Price  is_increasing
3  2021-01-04  32084.61              1
4  2021-01-05  34105.46              1
5  2021-01-06  36910.18              1
6  2021-01-07  39505.51              1
7  2021-01-08  40809.93              1

Upvotes: 4

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