這是 Python/ Pandas 的延續(xù)：Finding a left and right max我有一個數(shù)據(jù)框，其中包含數(shù)據(jù)時間表。這是一個例子：idx Q12000? ? ? Q22000? ? ? Q32000? ? ? Q42000? ? ? Q12001? ? ? Q22001? ? ? Q32001? ? ?Q42001? ? ? Q12002? ? ? Q22002? ? ? Q32002? ? ? Q420020? ?4085280.0? ?4114911.0? ?4108089.0? ?4111713.0? ?4055699.0? ?4076430.0? ?4043219.0? 4039370.0? ?4201158.0? ?4243119.0? ?4231823.0? ?4254681.01? ?21226.0? ? ?21566.0? ? ?21804.0? ? ?22072.0? ? ?21924.0? ? ?23232.0? ? ?22748.0? ? 22258.0? ? ?22614.0? ? ?22204.0? ? ?22500.0? ? ?22660.0? ? ?2? ?96400.0? ? ?102000.0? ? 98604.0? ? ?97086.0? ? ?96354.0? ? ?103054.0? ? 97824.0? ? 95958.0? ? ?115938.0? ? 123064.0? ? 120406.0? ? 120648.0? ?3? ?23820.0? ? ?24116.0? ? ?24186.0? ? ?23726.0? ? ?23504.0? ? ?23574.0? ? ?23162.0? ? 23078.0? ? ?22306.0? ? ?22334.0? ? ?22152.0? ? ?22080.0? ? ?4? ?7838.0? ? ? 7906.0? ? ? 7714.0? ? ? 7676.0? ? ? 7480.0? ? ? 7520.0? ? ? 7102.0? ? ?6722.0? ? ? 8324.0? ? ? 8166.0? ? ? 8208.0? ? ? 8326.0? ?為了進(jìn)行分析，我需要計算每行的以下值：nadir：最低點(diǎn)（分鐘）nadir_qtr：最低點(diǎn)發(fā)生的季度峰值前：最低點(diǎn)之前的最高點(diǎn)pre-peak_qtr：預(yù)峰值發(fā)生的季度峰后：最低點(diǎn)后的最高點(diǎn)post-peak_qtr：后峰值發(fā)生的季度在上一篇文章的幫助下，我使用了以下輔助函數(shù)：from io import StringIOimport pandas as pddef calc_nadir(s):? ? assert isinstance(s, pd.Series)? ? return s.min()def calc_nadir_qtr(s):? ? return s.argmin()def calc_pre_peak(s):? ? return s[ : s.argmin()].max()def calc_pre_peak_quarter(s):? ? try:? ? ? ? qtr = s[ : s.argmin()].argmax()? ? except:? ? ? ? qtr = None? ? return qtrdef calc_post_peak(s):? ? return s[s.argmin() : ].max()def calc_post_peak_qtr(s):? ? return s[s.argmin() : ].argmax() + s.argmin()nadir = df.apply(lambda x: calc_nadir(x), axis=1).rename('nadir')nadir_qtr = df.apply(lambda x: calc_nadir_qtr(x), axis=1).rename('nadir_qtr')我遇到的麻煩是第二行。將最低點(diǎn)作為第一列沒有意義，因此我更改了上面的代碼，只獲取前幾列之后的最低點(diǎn)。
1 回答
DIEA
TA貢獻(xiàn)1820條經(jīng)驗(yàn) 獲得超3個贊
您可以僅在第一列之后進(jìn)行選擇.iloc[:1,:]，并使用一堆 pandas 方法，例如.min、.max、等：idxminidxmax
df['nadir'] = df.iloc[:,1:].min(axis=1)
df['nadir_qtr'] = df.iloc[:,1:].idxmin(axis=1).apply(lambda x: df.columns.get_loc(x))
df['new'] = [df.iloc[i].values for i in df.index]
df['pre_peak'] = df.apply(lambda x: max(x['new'][0:x['nadir_qtr']]), axis=1)
df['post_peak'] = df.apply(lambda x: max(x['new'][x['nadir_qtr']:]), axis=1)
df['pre_peak_qtr'] = pd.Series([s[i] for i, s in zip(df.index, df['pre_peak'].apply(
lambda x: [i for i in (df.iloc[:,0:-6] == x)
.idxmax(axis=1)]))]).apply(lambda x: df.columns.get_loc(x))
df['post_peak_qtr'] = pd.Series([s[i] for i, s in zip(df.index, df['post_peak'].apply(
lambda x: [i for i in (df.iloc[:,0:-6] == x)
.idxmax(axis=1)]))]).apply(lambda x: df.columns.get_loc(x))
df_new = df[['nadir', 'nadir_qtr', 'pre_peak', 'pre_peak_qtr', 'post_peak', 'post_peak_qtr']]
df_new
Out[1]:
nadir nadir_qtr pre_peak pre_peak_qtr post_peak post_peak_qtr
idx
0 4039370.0 7 4114911.0 1 4254681.0 11
1 21566.0 1 21226.0 0 23232.0 5
2 95958.0 7 103054.0 5 123064.0 9
3 22080.0 11 24186.0 2 22080.0 11
4 6722.0 7 7906.0 1 8326.0 11
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