Allentro
Reputation: 416
Edit rows of a dataframe given one column has a certain value
I have a large dataframe df_trial, with a row nammed reaction. Within this there are five values which this may be: 152Gd-p, 154Gd-p, 155Gd-p, 156Gd-p, 15Gd-p, 158Gd-p, 160Gd-p. The following column contains some irrelevant information for this step, however the following columns I wish to be multiplied by a constant depending on the string present in 'reaction'. I have tried to apply this as:
for index, row in df_trial.iterrows():
if row['reaction'] == '152Gd-p':
row[2:]*=0.002
if row['reaction'] == '154Gd-p':
row[2:]*=0.0218
if row['reaction'] == '155Gd-p':
row[2:]*=0.148
if row['reaction'] == '156Gd-p':
row[2:]*=0.2047
if row['reaction'] == '157Gd-p':
row[2:]*=0.1565
if row['reaction'] == '158Gd-p':
row[2:]*=0.2484
if row['reaction'] == '160Gd-p':
row[2:]*=0.2186
This is however not multiplying the values in the rows.
Here is a sample of how my dataframe looks:
reaction product 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 ... 35.5 36.0 36.5 37.0 37.5 38.0 38.5 39.0 39.5 40.0
81 155Gd-p 062150.tot 0.0 0.0 0.0 0.0 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 ... 1.101530e-02 1.253720e-02 1.404620e-02 1.562480e-02 1.713510e-02 1.855860e-02 1.989160e-02 2.113160e-02 2.228710e-02 2.333700e-02
82 155Gd-p 065156.L00 0.0 0.0 0.0 0.0 2.842720e-10 4.331690e-09 3.176340e-08 1.873100e-03 ... 1.836500e-01 1.803630e-01 1.728360e-01 1.606180e-01 1.685970e-01 1.679980e-01 1.639340e-01 1.538330e-01 1.639280e-01 1.656980e-01
83 155Gd-p 063149.tot 0.0 0.0 0.0 0.0 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 ... 6.990920e+00 7.877440e+00 8.781220e+00 9.594980e+00 1.034780e+01 1.097080e+01 1.156940e+01 1.196730e+01 1.230900e+01 1.241800e+01
84 155Gd-p 061146.tot 0.0 0.0 0.0 0.0 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 ... 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 9.529110e-04
85 155Gd-p 061147.tot 0.0 0.0 0.0 0.0 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 ... 1.000000e-07 1.000000e-07 1.000000e-07 1.000000e-07 1.000000e-07 1.063060e-03 1.130870e-03 1.172590e-03 1.180610e-03 1.165960e-03
86 155Gd-p 062151.tot 0.0 0.0 0.0 0.0 0.000000e+00 0.000000e+00 1.000000e-07 1.000000e-07 ... 1.041300e-03 1.076720e-03 1.090690e-03 1.109420e-03 1.137780e-03 1.135450e-03 1.128680e-03 1.149190e-03 1.143860e-03 1.150390e-03
87 155Gd-p 063154.L00 0.0 0.0 0.0 0.0 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 ... 3.173660e+00 3.444480e+00 3.724070e+00 4.007600e+00 4.323930e+00 4.673050e+00 4.971810e+00 5.346970e+00 5.661720e+00 6.060110e+00
88 155Gd-p 064150.tot 0.0 0.0 0.0 0.0 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 ... 2.579220e-03 4.992070e-03 9.679620e-03 2.035170e-02 3.447910e-02 5.437450e-02 9.089650e-02 1.471190e-01 2.126190e-01 2.896810e-01
89 155Gd-p 064154.tot 0.0 0.0 0.0 0.0 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 ... 2.337670e+02 2.386140e+02 2.390300e+02 2.431350e+02 2.413420e+02 2.446070e+02 2.421500e+02 2.447660e+02 2.425080e+02 2.446910e+02
90 155Gd-p 062148.tot 0.0 0.0 0.0 0.0 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 ... 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.172560e-03 1.722920e-03
91 155Gd-p 061148.tot 0.0 0.0 0.0 0.0 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 ... 1.000000e-07 1.000000e-07 1.000000e-07 1.000000e-07 1.000000e-07 6.300980e-05 6.278910e-05 6.094680e-05 5.998620e-05 5.900480e-05
92 155Gd-p 063153.tot 0.0 0.0 0.0 0.0 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 ... 3.136120e+00 3.390600e+00 3.631630e+00 3.958020e+00 4.197170e+00 4.564750e+00 4.762130e+00 4.948580e+00 5.314960e+00 5.549590e+00
93 155Gd-p 063152.tot 0.0 0.0 0.0 0.0 1.000000e-07 1.000000e-07 1.000000e-07 1.000000e-07 ... 2.371420e+00 2.502180e+00 2.629100e+00 2.699470e+00 2.818750e+00 2.972010e+00 3.188610e+00 3.416830e+00 3.648320e+00 3.884690e+00
94 155Gd-p 065151.tot 0.0 0.0 0.0 0.0 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 ... 6.969380e+01 1.072870e+02 1.483310e+02 1.996210e+02 2.548350e+02 3.046590e+02 3.501350e+02 3.969820e+02 4.371780e+02 4.748900e+02
95 155Gd-p 063150.L01 0.0 0.0 0.0 0.0 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 ... 2.297790e+00 2.303140e+00 2.279280e+00 2.212180e+00 2.171240e+00 2.134990e+00 2.086730e+00 2.017590e+00 1.979700e+00 1.957500e+00
96 155Gd-p 065152.tot 0.0 0.0 0.0 0.0 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 ... 7.834620e+02 7.493000e+02 7.095910e+02 6.583400e+02 6.050390e+02 5.525560e+02 5.055030e+02 4.521510e+02 4.095180e+02 3.664050e+02
Obviously in the full dataframe it includes rows with all of the reactions in.
Upvotes: 1
Views: 56
Answers (1)
Alexandre B.
Reputation: 5502
You can define a function to multiply each row and then apply it to each row with apply.
Also, to improve the conditions, you can set elif instead of if. That prevents testing all the conditions each time.
In the answer, I use a dictionary to save the coefficient. Then, I call it from the reaction key.
Here the code:
# Coefficients
multi_coef = {
'152Gd-p': 0.002,
'154Gd-p': 0.0218,
'155Gd-p': 0.148,
'156Gd-p': 0.2047,
'157Gd-p': 0.1565,
'158Gd-p': 0.2484,
'160Gd-p': 0.2186,
}
# Get columns name
columns = df.columns
# Function to apply to each row
def multiply_coef(row):
# If the reaction name is in the dictionary
if row.reaction in multi_coef.keys():
# Multiply by the reaction coefficient
row[columns[2:]] = row[columns[2:]] * multi_coef[row.reaction]
return row
# Apply the function (axis = 1 means over rows)
new_df = df.apply(multiply_coef, axis=1)
print(new_df)
# reaction product 0.5 1.0 1.5 2.0 ... 37.5 38.0 38.5 39.0 39.5 40.0
# 0 155Gd-p 062150.tot 0.0 0.0 0.0 0.0 ... 2.535995e-03 0.002747 0.002944 0.003127 0.003298 0.003454
# 1 155Gd-p 065156.L00 0.0 0.0 0.0 0.0 ... 2.495236e-02 0.024864 0.024262 0.022767 0.024261 0.024523
# 2 155Gd-p 063149.tot 0.0 0.0 0.0 0.0 ... 1.531474e+00 1.623678 1.712271 1.771160 1.821732 1.837864
# 3 155Gd-p 061146.tot 0.0 0.0 0.0 0.0 ... 0.000000e+00 0.000000 0.000000 0.000000 0.000000 0.000141
# 4 155Gd-p 061147.tot 0.0 0.0 0.0 0.0 ... 1.480000e-08 0.000157 0.000167 0.000174 0.000175 0.000173
# 5 155Gd-p 062151.tot 0.0 0.0 0.0 0.0 ... 1.683914e-04 0.000168 0.000167 0.000170 0.000169 0.000170
# 6 155Gd-p 063154.L00 0.0 0.0 0.0 0.0 ... 6.399416e-01 0.691611 0.735828 0.791352 0.837935 0.896896
# 7 155Gd-p 064150.tot 0.0 0.0 0.0 0.0 ... 5.102907e-03 0.008047 0.013453 0.021774 0.031468 0.042873
# 8 155Gd-p 064154.tot 0.0 0.0 0.0 0.0 ... 3.571862e+01 36.201836 35.838200 36.225368 35.891184 36.214268
# 9 155Gd-p 062148.tot 0.0 0.0 0.0 0.0 ... 0.000000e+00 0.000000 0.000000 0.000000 0.000174 0.000255
# 10 155Gd-p 061148.tot 0.0 0.0 0.0 0.0 ... 1.480000e-08 0.000009 0.000009 0.000009 0.000009 0.000009
# 11 155Gd-p 063153.tot 0.0 0.0 0.0 0.0 ... 6.211812e-01 0.675583 0.704795 0.732390 0.786614 0.821339
# 12 155Gd-p 063152.tot 0.0 0.0 0.0 0.0 ... 4.171750e-01 0.439857 0.471914 0.505691 0.539951 0.574934
# 13 155Gd-p 065151.tot 0.0 0.0 0.0 0.0 ... 3.771558e+01 45.089532 51.819980 58.753336 64.702344 70.283720
# 14 155Gd-p 063150.L01 0.0 0.0 0.0 0.0 ... 3.213435e-01 0.315979 0.308836 0.298603 0.292996 0.289710
# 15 155Gd-p 065152.tot 0.0 0.0 0.0 0.0 ... 8.954577e+01 81.778288 74.814444 66.918348 60.608664 54.227940
Upvotes: 1
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