[英]Splitting a Column on Positive and Negative values
如何根據條件將列拆分為兩個不同的列,但保留一個鍵? 例如
col1 col2 time value
0 A sdf 16:00:00 100
1 B sdh 17:00:00 -40
2 A sf 18:00:45 300
3 D sfd 20:04:33 -89
我想要一個像這樣的新數據幀
time main_val sub_val
0 16:00:00 100 NaN
1 17:00:00 NaN -40
2 18:00:45 300 NaN
3 20:04:33 NaN -89
你可以使用mask
:
mask = df['value'] < 0
df['main_val'] = df['value'].mask(mask)
df['sub_val'] = df['value'].mask(~mask)
df = df.drop(['col1','col2', 'value'], axis=1)
print (df)
time main_val sub_val
0 16:00:00 100.0 NaN
1 17:00:00 NaN -40.0
2 18:00:45 300.0 NaN
3 20:04:33 NaN -89.0
我使用pd.get_dummies
, mask
和mul
n = {True: 'main_val', False: 'sub_val'}
m = pd.get_dummies(df.value > 0).rename(columns=n)
df.drop('value', 1).join(m.mask(m == 0).mul(df.value, 0))
col1 col2 time sub_val main_val
0 A sdf 16:00:00 NaN 100.0
1 B sdh 17:00:00 -40.0 NaN
2 A sf 18:00:45 NaN 300.0
3 D sfd 20:04:33 -89.0 NaN
如果你看一下m.mask(m == 0)
,它就會變得更加清晰。
sub_val main_val
0 NaN 1.0
1 1.0 NaN
2 NaN 1.0
3 1.0 NaN
pd.get_dummies
給出了0和1。 然后我把所有的零都寫成np.nan
。 當我乘以mul
, df.value
列會在這兩列中進行廣播,我們得到了結果。 我使用join
將它附加回數據幀。
我們可以通過numpy
來提高速度
v = df.value.values[:, None]
m = v > 0
n = np.where(np.hstack([m, ~m]), v, np.nan)
c = ['main_val', 'sub_val']
df.drop('value', 1).join(pd.DataFrame(n, df.index, c))
sub_val main_val
0 NaN 1.0
1 1.0 NaN
2 NaN 1.0
3 1.0 NaN
這甚至可以通過數據透視表完成
df['Val1'] = np.where(df.value >=0,'main_val','sub_val' )
df = pd.pivot_table(df,index='time', values='value',
columns=['Val1'], aggfunc=np.sum).reset_index()
df = pd.DataFrame(df.values)
df.columns = ['time','main_val','sub_val']
import pandas as pd
df = pd.DataFrame({'col1':['A', 'B', 'A', 'D'],
'col2':['sdf', 'sdh', 'sf', 'sfd'],
'time':['16:00:00', '17:00:00', '18:00:45', '20:04:33'],
'value':[100, -40, 300, -89]})
print(df)
col1 col2 time value
0 A sdf 16:00:00 100
1 B sdh 17:00:00 -40
2 A sf 18:00:45 300
3 D sfd 20:04:33 -89
。
new = df[['time']].copy()
new['main_val'] = df['value'].where(df['value'] > 0)
new['sub_val'] = df['value'].where(df['value'] < 0)
print(new)
time main_val sub_val
0 16:00:00 100.0 NaN
1 17:00:00 NaN -40.0
2 18:00:45 300.0 NaN
3 20:04:33 NaN -89.0
使用numpy在創建新列時從nans或列值中選擇(比df.where快一點,靈感來自Kamaraju Kusumanchi的優秀答案)
vals = df.value.values
nans = np.full(len(df), np.nan)
df2 = df[['time']].copy()
df2['main_val'] = np.where(vals < 0, nans, vals)
df2['sub_val'] = np.where(vals >= 0, nans, vals)
print(df2)
time main_val sub_val
0 16:00:00 100.0 NaN
1 17:00:00 NaN -40.0
2 18:00:45 300.0 NaN
3 20:04:33 NaN -89.0
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