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creating a new column by counting element in range of rows using pyhton
Peak detection in a 2D arrayAdding new column to existing DataFrame in Python pandas“Large data” work flows using pandasHow do I get the row count of a pandas DataFrame?Select rows from a DataFrame based on values in a column in pandaspandas create new column based on values from other columnsHow to count the element in a column and take the result as a new column?Create a new column that counts “;” in a rowHow to sum counted pandas dataframe column with multiple conditions row-wiseComparing rows in 2 dataframes and counting number of similar columns
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty height:90px;width:728px;box-sizing:border-box;
I am creating a permutation that contains WL for each Match as follow:
N_Match = 3
data = pd.DataFrame(list(itertools.product("WL", repeat=N_Match)))
Since I used N_match = 3 the output of dataframe will have 3 columns:
I would like to count number of W and L for each row in all columns.
I will create a new column as follow:
If number of W is greater than no. of L, the formula that i will use for the cell is 1/2^(N_Match), else 0
HOw can I create that column as shown in the following sample:
in this case, 0.125 is calculated as 1/2^N_Match,
Thanks in advance for the advice.
Regards,
Zep
python pandas
asked Mar 25 at 7:52
ZephyrZephyr
533313
add a comment |
I am creating a permutation that contains WL for each Match as follow:
N_Match = 3
data = pd.DataFrame(list(itertools.product("WL", repeat=N_Match)))
Since I used N_match = 3 the output of dataframe will have 3 columns:
I would like to count number of W and L for each row in all columns.
I will create a new column as follow:
If number of W is greater than no. of L, the formula that i will use for the cell is 1/2^(N_Match), else 0
HOw can I create that column as shown in the following sample:
in this case, 0.125 is calculated as 1/2^N_Match,
Thanks in advance for the advice.
Regards,
Zep
python pandas
asked Mar 25 at 7:52
ZephyrZephyr
533313
add a comment |
I am creating a permutation that contains WL for each Match as follow:
N_Match = 3
data = pd.DataFrame(list(itertools.product("WL", repeat=N_Match)))
Since I used N_match = 3 the output of dataframe will have 3 columns:
I would like to count number of W and L for each row in all columns.
I will create a new column as follow:
If number of W is greater than no. of L, the formula that i will use for the cell is 1/2^(N_Match), else 0
HOw can I create that column as shown in the following sample:
in this case, 0.125 is calculated as 1/2^N_Match,
Thanks in advance for the advice.
Regards,
Zep
python pandas
asked Mar 25 at 7:52
ZephyrZephyr
533313
I am creating a permutation that contains WL for each Match as follow:
N_Match = 3
data = pd.DataFrame(list(itertools.product("WL", repeat=N_Match)))
Since I used N_match = 3 the output of dataframe will have 3 columns:
I would like to count number of W and L for each row in all columns.
I will create a new column as follow:
If number of W is greater than no. of L, the formula that i will use for the cell is 1/2^(N_Match), else 0
HOw can I create that column as shown in the following sample:
in this case, 0.125 is calculated as 1/2^N_Match,
Thanks in advance for the advice.
Regards,
Zep
python pandas
python pandas
asked Mar 25 at 7:52
ZephyrZephyr
533313
asked Mar 25 at 7:52
ZephyrZephyr
533313
asked Mar 25 at 7:52
ZephyrZephyr
533313
asked Mar 25 at 7:52
ZephyrZephyr
533313
asked Mar 25 at 7:52
ZephyrZephyr
533313
533313
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Use numpy.where with count number of matched values by sum of Trues values by DataFrame.eq:
mask = data.eq('W').sum(axis=1) > data.eq('L').sum(axis=1)
data['Prob'] = np.where(mask, 1/2**(N_Match), 0)
print (data)
0 1 2 Prob
0 W W W 0.125
1 W W L 0.125
2 W L W 0.125
3 W L L 0.000
4 L W W 0.125
5 L W L 0.000
6 L L W 0.000
7 L L L 0.000
answered Mar 25 at 7:56
jezraeljezrael
381k27373445
add a comment |
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1 Answer
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active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Use numpy.where with count number of matched values by sum of Trues values by DataFrame.eq:
mask = data.eq('W').sum(axis=1) > data.eq('L').sum(axis=1)
data['Prob'] = np.where(mask, 1/2**(N_Match), 0)
print (data)
0 1 2 Prob
0 W W W 0.125
1 W W L 0.125
2 W L W 0.125
3 W L L 0.000
4 L W W 0.125
5 L W L 0.000
6 L L W 0.000
7 L L L 0.000
answered Mar 25 at 7:56
jezraeljezrael
381k27373445
add a comment |
Use numpy.where with count number of matched values by sum of Trues values by DataFrame.eq:
mask = data.eq('W').sum(axis=1) > data.eq('L').sum(axis=1)
data['Prob'] = np.where(mask, 1/2**(N_Match), 0)
print (data)
0 1 2 Prob
0 W W W 0.125
1 W W L 0.125
2 W L W 0.125
3 W L L 0.000
4 L W W 0.125
5 L W L 0.000
6 L L W 0.000
7 L L L 0.000
answered Mar 25 at 7:56
jezraeljezrael
381k27373445
add a comment |
Use numpy.where with count number of matched values by sum of Trues values by DataFrame.eq:
mask = data.eq('W').sum(axis=1) > data.eq('L').sum(axis=1)
data['Prob'] = np.where(mask, 1/2**(N_Match), 0)
print (data)
0 1 2 Prob
0 W W W 0.125
1 W W L 0.125
2 W L W 0.125
3 W L L 0.000
4 L W W 0.125
5 L W L 0.000
6 L L W 0.000
7 L L L 0.000
answered Mar 25 at 7:56
jezraeljezrael
381k27373445
Use numpy.where with count number of matched values by sum of Trues values by DataFrame.eq:
mask = data.eq('W').sum(axis=1) > data.eq('L').sum(axis=1)
data['Prob'] = np.where(mask, 1/2**(N_Match), 0)
print (data)
0 1 2 Prob
0 W W W 0.125
1 W W L 0.125
2 W L W 0.125
3 W L L 0.000
4 L W W 0.125
5 L W L 0.000
6 L L W 0.000
7 L L L 0.000
answered Mar 25 at 7:56
jezraeljezrael
381k27373445
edited Mar 25 at 8:01
answered Mar 25 at 7:56
jezraeljezrael
381k27373445
answered Mar 25 at 7:56
jezraeljezrael
381k27373445
answered Mar 25 at 7:56
jezraeljezrael
381k27373445
381k27373445
add a comment |
add a comment |
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