Calculate Time in pandas by extracting
0
I have a set of data
Time1 Time2
XY40M XY35M
XY5H XY45M
XY30M XY20M
XY1H XY2H
XY1H30M XY2H
I have to calculate the Total time in minutes
Time1+Time2
75
345
50
180
210
How can i derive this?
python pandas
edited Nov 24 '18 at 12:50
user10465355
1,9432416
asked Mar 11 '18 at 5:46
Tom J MuthirenthiTom J Muthirenthi
1,14411328
add a comment |
0
I have a set of data
Time1 Time2
XY40M XY35M
XY5H XY45M
XY30M XY20M
XY1H XY2H
XY1H30M XY2H
I have to calculate the Total time in minutes
Time1+Time2
75
345
50
180
210
How can i derive this?
python pandas
edited Nov 24 '18 at 12:50
user10465355
1,9432416
asked Mar 11 '18 at 5:46
Tom J MuthirenthiTom J Muthirenthi
1,14411328
add a comment |
0
0
0
I have a set of data
Time1 Time2
XY40M XY35M
XY5H XY45M
XY30M XY20M
XY1H XY2H
XY1H30M XY2H
I have to calculate the Total time in minutes
Time1+Time2
75
345
50
180
210
How can i derive this?
python pandas
edited Nov 24 '18 at 12:50
user10465355
1,9432416
asked Mar 11 '18 at 5:46
Tom J MuthirenthiTom J Muthirenthi
1,14411328
I have a set of data
Time1 Time2
XY40M XY35M
XY5H XY45M
XY30M XY20M
XY1H XY2H
XY1H30M XY2H
I have to calculate the Total time in minutes
Time1+Time2
75
345
50
180
210
How can i derive this?
python pandas
python pandas
edited Nov 24 '18 at 12:50
user10465355
1,9432416
asked Mar 11 '18 at 5:46
Tom J MuthirenthiTom J Muthirenthi
1,14411328
edited Nov 24 '18 at 12:50
user10465355
1,9432416
asked Mar 11 '18 at 5:46
Tom J MuthirenthiTom J Muthirenthi
1,14411328
edited Nov 24 '18 at 12:50
user10465355
1,9432416
edited Nov 24 '18 at 12:50
user10465355
1,9432416
edited Nov 24 '18 at 12:50
user10465355
1,9432416
1,9432416
asked Mar 11 '18 at 5:46
Tom J MuthirenthiTom J Muthirenthi
1,14411328
asked Mar 11 '18 at 5:46
Tom J MuthirenthiTom J Muthirenthi
1,14411328
asked Mar 11 '18 at 5:46
Tom J MuthirenthiTom J Muthirenthi
1,14411328
1,14411328
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
2
Use str.extract with numpy.where:
a = df['Time1'].str.extract('(d+[MH])', expand=False)
a1 = a.str[:-1].astype(int)
b = df['Time2'].str.extract('(d+[MH])', expand=False)
b1 = b.str[:-1].astype(int)
df['Time'] = np.where(a.str[-1] == 'H', a1 * 60, a1) + np.where(b.str[-1] == 'H', b1 * 60, b1)
Another solution:
a = df['Time1'].str.extract('(d+)([MH])', expand=True)
a1 = a[0].astype(int)
b = df['Time2'].str.extract('(d+)([MH])', expand=True)
b1 = b[0].astype(int)
df['Time'] = np.where(a[1] == 'H', a1 * 60, a1) + np.where(b[1] == 'H', b1 * 60, b1)
print (df)
Time1 Time2 Time
0 XY40M XY35M 75
1 XY5H XY45M 345
2 XY30M XY20M 50
3 XY1H XY2H 180
EDIT:
a = df['Time1'].str.extract('(d+)([MH])(d*)([M]*)', expand=True)
a1 = a[[0,2]].replace('', 0).astype(int)
b = df['Time2'].str.extract('(d+)([MH])(d*)([M]*)', expand=True)
b1 = b[[0,2]].replace('', 0).astype(int)
df['Time'] = np.where(a[1] == 'H', a1[0] * 60, a1[0]) + a1[2] +
np.where(b[1] == 'H', b1[0] * 60, b1[0]) + b1[2]
print (df)
Time1 Time2 Time
0 XY40M XY35M 75
1 XY5H XY45M 345
2 XY30M XY20M 50
3 XY1H XY2H 180
4 XY1H30M XY2H 210
answered Mar 11 '18 at 5:54
jezraeljezrael
337k25281357
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
2
Use str.extract with numpy.where:
a = df['Time1'].str.extract('(d+[MH])', expand=False)
a1 = a.str[:-1].astype(int)
b = df['Time2'].str.extract('(d+[MH])', expand=False)
b1 = b.str[:-1].astype(int)
df['Time'] = np.where(a.str[-1] == 'H', a1 * 60, a1) + np.where(b.str[-1] == 'H', b1 * 60, b1)
Another solution:
a = df['Time1'].str.extract('(d+)([MH])', expand=True)
a1 = a[0].astype(int)
b = df['Time2'].str.extract('(d+)([MH])', expand=True)
b1 = b[0].astype(int)
df['Time'] = np.where(a[1] == 'H', a1 * 60, a1) + np.where(b[1] == 'H', b1 * 60, b1)
print (df)
Time1 Time2 Time
0 XY40M XY35M 75
1 XY5H XY45M 345
2 XY30M XY20M 50
3 XY1H XY2H 180
EDIT:
a = df['Time1'].str.extract('(d+)([MH])(d*)([M]*)', expand=True)
a1 = a[[0,2]].replace('', 0).astype(int)
b = df['Time2'].str.extract('(d+)([MH])(d*)([M]*)', expand=True)
b1 = b[[0,2]].replace('', 0).astype(int)
df['Time'] = np.where(a[1] == 'H', a1[0] * 60, a1[0]) + a1[2] +
np.where(b[1] == 'H', b1[0] * 60, b1[0]) + b1[2]
print (df)
Time1 Time2 Time
0 XY40M XY35M 75
1 XY5H XY45M 345
2 XY30M XY20M 50
3 XY1H XY2H 180
4 XY1H30M XY2H 210
answered Mar 11 '18 at 5:54
jezraeljezrael
337k25281357
add a comment |
2
Use str.extract with numpy.where:
a = df['Time1'].str.extract('(d+[MH])', expand=False)
a1 = a.str[:-1].astype(int)
b = df['Time2'].str.extract('(d+[MH])', expand=False)
b1 = b.str[:-1].astype(int)
df['Time'] = np.where(a.str[-1] == 'H', a1 * 60, a1) + np.where(b.str[-1] == 'H', b1 * 60, b1)
Another solution:
a = df['Time1'].str.extract('(d+)([MH])', expand=True)
a1 = a[0].astype(int)
b = df['Time2'].str.extract('(d+)([MH])', expand=True)
b1 = b[0].astype(int)
df['Time'] = np.where(a[1] == 'H', a1 * 60, a1) + np.where(b[1] == 'H', b1 * 60, b1)
print (df)
Time1 Time2 Time
0 XY40M XY35M 75
1 XY5H XY45M 345
2 XY30M XY20M 50
3 XY1H XY2H 180
EDIT:
a = df['Time1'].str.extract('(d+)([MH])(d*)([M]*)', expand=True)
a1 = a[[0,2]].replace('', 0).astype(int)
b = df['Time2'].str.extract('(d+)([MH])(d*)([M]*)', expand=True)
b1 = b[[0,2]].replace('', 0).astype(int)
df['Time'] = np.where(a[1] == 'H', a1[0] * 60, a1[0]) + a1[2] +
np.where(b[1] == 'H', b1[0] * 60, b1[0]) + b1[2]
print (df)
Time1 Time2 Time
0 XY40M XY35M 75
1 XY5H XY45M 345
2 XY30M XY20M 50
3 XY1H XY2H 180
4 XY1H30M XY2H 210
answered Mar 11 '18 at 5:54
jezraeljezrael
337k25281357
add a comment |
2
2
2
Use str.extract with numpy.where:
a = df['Time1'].str.extract('(d+[MH])', expand=False)
a1 = a.str[:-1].astype(int)
b = df['Time2'].str.extract('(d+[MH])', expand=False)
b1 = b.str[:-1].astype(int)
df['Time'] = np.where(a.str[-1] == 'H', a1 * 60, a1) + np.where(b.str[-1] == 'H', b1 * 60, b1)
Another solution:
a = df['Time1'].str.extract('(d+)([MH])', expand=True)
a1 = a[0].astype(int)
b = df['Time2'].str.extract('(d+)([MH])', expand=True)
b1 = b[0].astype(int)
df['Time'] = np.where(a[1] == 'H', a1 * 60, a1) + np.where(b[1] == 'H', b1 * 60, b1)
print (df)
Time1 Time2 Time
0 XY40M XY35M 75
1 XY5H XY45M 345
2 XY30M XY20M 50
3 XY1H XY2H 180
EDIT:
a = df['Time1'].str.extract('(d+)([MH])(d*)([M]*)', expand=True)
a1 = a[[0,2]].replace('', 0).astype(int)
b = df['Time2'].str.extract('(d+)([MH])(d*)([M]*)', expand=True)
b1 = b[[0,2]].replace('', 0).astype(int)
df['Time'] = np.where(a[1] == 'H', a1[0] * 60, a1[0]) + a1[2] +
np.where(b[1] == 'H', b1[0] * 60, b1[0]) + b1[2]
print (df)
Time1 Time2 Time
0 XY40M XY35M 75
1 XY5H XY45M 345
2 XY30M XY20M 50
3 XY1H XY2H 180
4 XY1H30M XY2H 210
answered Mar 11 '18 at 5:54
jezraeljezrael
337k25281357
Use str.extract with numpy.where:
a = df['Time1'].str.extract('(d+[MH])', expand=False)
a1 = a.str[:-1].astype(int)
b = df['Time2'].str.extract('(d+[MH])', expand=False)
b1 = b.str[:-1].astype(int)
df['Time'] = np.where(a.str[-1] == 'H', a1 * 60, a1) + np.where(b.str[-1] == 'H', b1 * 60, b1)
Another solution:
a = df['Time1'].str.extract('(d+)([MH])', expand=True)
a1 = a[0].astype(int)
b = df['Time2'].str.extract('(d+)([MH])', expand=True)
b1 = b[0].astype(int)
df['Time'] = np.where(a[1] == 'H', a1 * 60, a1) + np.where(b[1] == 'H', b1 * 60, b1)
print (df)
Time1 Time2 Time
0 XY40M XY35M 75
1 XY5H XY45M 345
2 XY30M XY20M 50
3 XY1H XY2H 180
EDIT:
a = df['Time1'].str.extract('(d+)([MH])(d*)([M]*)', expand=True)
a1 = a[[0,2]].replace('', 0).astype(int)
b = df['Time2'].str.extract('(d+)([MH])(d*)([M]*)', expand=True)
b1 = b[[0,2]].replace('', 0).astype(int)
df['Time'] = np.where(a[1] == 'H', a1[0] * 60, a1[0]) + a1[2] +
np.where(b[1] == 'H', b1[0] * 60, b1[0]) + b1[2]
print (df)
Time1 Time2 Time
0 XY40M XY35M 75
1 XY5H XY45M 345
2 XY30M XY20M 50
3 XY1H XY2H 180
4 XY1H30M XY2H 210
answered Mar 11 '18 at 5:54
jezraeljezrael
337k25281357
edited Mar 11 '18 at 6:37
answered Mar 11 '18 at 5:54
jezraeljezrael
337k25281357
answered Mar 11 '18 at 5:54
jezraeljezrael
337k25281357
answered Mar 11 '18 at 5:54
jezraeljezrael
337k25281357
337k25281357
add a comment |
add a comment |
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