Mixing
Populating a data frame using from separate table using loc
data1={'Policy Number':['FSH1235456','FSH7643643','CHH123124','CHH123145252','CHH124124'],'State':['FL','TX','GA','TX','TX'],'TERR':[1,2,3,4,5]}
data2={'TERR':[1,2,3,4,5],'CHH':[0,.15,.65,.35,.20],'FSH':[0,.15,.25,.35,.20]}
output={'Policy Number':['FSH1235456','FSH7643643','CHH123124','CHH123145252','CHH124124'],'State':['FL','TX','GA','TX','TX'],'TERR':[1,2,3,4,5],'Test':[0,.15,0,0,0]}
df1=pd.DataFrame(data1)
df2=pd.DataFrame(data2)
df3=pd.DataFrame(output)
Test data above.
I am trying to create a new column in df1 call it df1['Test'], which contains the values of df2['FSH'] based on the following criteria:
- The state is 'TX'
- The Policy Number contains 'FSH'
3.The value of df1["Terr"] = value of df2['TERR']
View df3 for correct output.
What I tried doing was the following;
if df1.State.any()=="TX":
if df1["Policy Number"].str.contains("FSH").any():
for i in df["TERR"]:
df1['% TERR']=df2.loc[[i],["FSH"]]
However, my output is riddled with NAN, as well as 1 unique correct answer.
I tried checking to ensure the correct i values were being fed into df2 via
print(df2.loc[[i],["FSH"]]
and it is printing correctly.
Any thoughts?
python pandas
asked Nov 19 '18 at 18:27
Bjc51192Bjc51192
527
add a comment |
data1={'Policy Number':['FSH1235456','FSH7643643','CHH123124','CHH123145252','CHH124124'],'State':['FL','TX','GA','TX','TX'],'TERR':[1,2,3,4,5]}
data2={'TERR':[1,2,3,4,5],'CHH':[0,.15,.65,.35,.20],'FSH':[0,.15,.25,.35,.20]}
output={'Policy Number':['FSH1235456','FSH7643643','CHH123124','CHH123145252','CHH124124'],'State':['FL','TX','GA','TX','TX'],'TERR':[1,2,3,4,5],'Test':[0,.15,0,0,0]}
df1=pd.DataFrame(data1)
df2=pd.DataFrame(data2)
df3=pd.DataFrame(output)
Test data above.
I am trying to create a new column in df1 call it df1['Test'], which contains the values of df2['FSH'] based on the following criteria:
- The state is 'TX'
- The Policy Number contains 'FSH'
3.The value of df1["Terr"] = value of df2['TERR']
View df3 for correct output.
What I tried doing was the following;
if df1.State.any()=="TX":
if df1["Policy Number"].str.contains("FSH").any():
for i in df["TERR"]:
df1['% TERR']=df2.loc[[i],["FSH"]]
However, my output is riddled with NAN, as well as 1 unique correct answer.
I tried checking to ensure the correct i values were being fed into df2 via
print(df2.loc[[i],["FSH"]]
and it is printing correctly.
Any thoughts?
python pandas
asked Nov 19 '18 at 18:27
Bjc51192Bjc51192
527
add a comment |
data1={'Policy Number':['FSH1235456','FSH7643643','CHH123124','CHH123145252','CHH124124'],'State':['FL','TX','GA','TX','TX'],'TERR':[1,2,3,4,5]}
data2={'TERR':[1,2,3,4,5],'CHH':[0,.15,.65,.35,.20],'FSH':[0,.15,.25,.35,.20]}
output={'Policy Number':['FSH1235456','FSH7643643','CHH123124','CHH123145252','CHH124124'],'State':['FL','TX','GA','TX','TX'],'TERR':[1,2,3,4,5],'Test':[0,.15,0,0,0]}
df1=pd.DataFrame(data1)
df2=pd.DataFrame(data2)
df3=pd.DataFrame(output)
Test data above.
I am trying to create a new column in df1 call it df1['Test'], which contains the values of df2['FSH'] based on the following criteria:
- The state is 'TX'
- The Policy Number contains 'FSH'
3.The value of df1["Terr"] = value of df2['TERR']
View df3 for correct output.
What I tried doing was the following;
if df1.State.any()=="TX":
if df1["Policy Number"].str.contains("FSH").any():
for i in df["TERR"]:
df1['% TERR']=df2.loc[[i],["FSH"]]
However, my output is riddled with NAN, as well as 1 unique correct answer.
I tried checking to ensure the correct i values were being fed into df2 via
print(df2.loc[[i],["FSH"]]
and it is printing correctly.
Any thoughts?
python pandas
asked Nov 19 '18 at 18:27
Bjc51192Bjc51192
527
data1={'Policy Number':['FSH1235456','FSH7643643','CHH123124','CHH123145252','CHH124124'],'State':['FL','TX','GA','TX','TX'],'TERR':[1,2,3,4,5]}
data2={'TERR':[1,2,3,4,5],'CHH':[0,.15,.65,.35,.20],'FSH':[0,.15,.25,.35,.20]}
output={'Policy Number':['FSH1235456','FSH7643643','CHH123124','CHH123145252','CHH124124'],'State':['FL','TX','GA','TX','TX'],'TERR':[1,2,3,4,5],'Test':[0,.15,0,0,0]}
df1=pd.DataFrame(data1)
df2=pd.DataFrame(data2)
df3=pd.DataFrame(output)
Test data above.
I am trying to create a new column in df1 call it df1['Test'], which contains the values of df2['FSH'] based on the following criteria:
- The state is 'TX'
- The Policy Number contains 'FSH'
3.The value of df1["Terr"] = value of df2['TERR']
View df3 for correct output.
What I tried doing was the following;
if df1.State.any()=="TX":
if df1["Policy Number"].str.contains("FSH").any():
for i in df["TERR"]:
df1['% TERR']=df2.loc[[i],["FSH"]]
However, my output is riddled with NAN, as well as 1 unique correct answer.
I tried checking to ensure the correct i values were being fed into df2 via
print(df2.loc[[i],["FSH"]]
and it is printing correctly.
Any thoughts?
python pandas
python pandas
asked Nov 19 '18 at 18:27
Bjc51192Bjc51192
527
asked Nov 19 '18 at 18:27
Bjc51192Bjc51192
527
asked Nov 19 '18 at 18:27
Bjc51192Bjc51192
527
asked Nov 19 '18 at 18:27
Bjc51192Bjc51192
527
asked Nov 19 '18 at 18:27
Bjc51192Bjc51192
527
527
add a comment |
add a comment |
4 Answers
4
active
oldest
votes
I dont know if this is the best or fastest solution but one option is to merge your 2 dataframes then filter based on your conditions then update:
new = df1.merge(df2, on='TERR')
mask = new[((new['State']=='TX') & (new['Policy Number'].str.contains('FSH')))]
df1['Test'] = 0
df1['Test'].update(mask['FSH'])
Policy Number State TERR Test
0 FSH1235456 FL 1 0.00
1 FSH7643643 TX 2 0.15
2 CHH123124 GA 3 0.00
3 CHH123145252 TX 4 0.00
4 CHH124124 TX 5 0.00
answered Nov 19 '18 at 18:39
ChrisChris
1,9411316
add a comment |
You can use numpy where by passing conditions,
cond1 = (df1['State'] == 'TX')
cond2 = (df1['Policy Number'].str.contains('FSH'))
cond3 = (df1["TERR"] == df2['TERR'])
df1['Test'] = np.where(cond1 & cond2 & cond3, df2['FSH'], 0)
Policy Number State TERR Test
0 FSH1235456 FL 1 0.00
1 FSH7643643 TX 2 0.15
2 CHH123124 GA 3 0.00
3 CHH123145252 TX 4 0.00
4 CHH124124 TX 5 0.00
answered Nov 19 '18 at 19:23
VaishaliVaishali
18k31028
This is a great solution, however how would you take into consideration the case where the shapes do not match exactly?
– Bjc51192
Nov 19 '18 at 20:32
You can use map when the shapes of dataframes do not match but in this case, the mapping is possible only based TERR columns. The other two conditions are still index based
– Vaishali
Nov 19 '18 at 20:37
add a comment |
Are you just trying to get the data from df2 into df1? If so, you could reshape df2 using melt and then do a merge.
df1['policy_prefix'] = df1['Policy Number'].str[:3]
df2 = df2.melt(id_vars='TERR', value_vars=['CHH', 'FSH'],
value_name='Test',
var_name='policy_prefix')
df1 = df1.merge(df2, on=['policy_prefix', 'TERR'])
if you only want this to apply to rows where the state is 'TX' then you could set the other values to null after the merge:
import numpy as np
df1.loc[df1.State!='TX', 'Test'] = np.nan
answered Nov 19 '18 at 18:40
RobertRobert
33429
add a comment |
Here is your solution:
# ... initialize df1 and df2 here
df3 = df1.join(df2.FSH) # Merge df1 and df2 into a single dataframe
df3 = df3.rename({"FSH": "TEST"}, axis=1) # Change column name
def set_tx_fsh(row):
if row.State == "TX" and "FSH" in row["Policy Number"]:
return row.TEST
else:
return 0
df3.TEST = df3.apply(set_tx_fsh, axis=1) # Set values in "TEST" column based on your condition
answered Nov 19 '18 at 18:47
jadelordjadelord
493511
add a comment |
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4 Answers
4
active
oldest
votes
4 Answers
4
active
oldest
votes
active
oldest
votes
active
oldest
votes
I dont know if this is the best or fastest solution but one option is to merge your 2 dataframes then filter based on your conditions then update:
new = df1.merge(df2, on='TERR')
mask = new[((new['State']=='TX') & (new['Policy Number'].str.contains('FSH')))]
df1['Test'] = 0
df1['Test'].update(mask['FSH'])
Policy Number State TERR Test
0 FSH1235456 FL 1 0.00
1 FSH7643643 TX 2 0.15
2 CHH123124 GA 3 0.00
3 CHH123145252 TX 4 0.00
4 CHH124124 TX 5 0.00
answered Nov 19 '18 at 18:39
ChrisChris
1,9411316
add a comment |
I dont know if this is the best or fastest solution but one option is to merge your 2 dataframes then filter based on your conditions then update:
new = df1.merge(df2, on='TERR')
mask = new[((new['State']=='TX') & (new['Policy Number'].str.contains('FSH')))]
df1['Test'] = 0
df1['Test'].update(mask['FSH'])
Policy Number State TERR Test
0 FSH1235456 FL 1 0.00
1 FSH7643643 TX 2 0.15
2 CHH123124 GA 3 0.00
3 CHH123145252 TX 4 0.00
4 CHH124124 TX 5 0.00
answered Nov 19 '18 at 18:39
ChrisChris
1,9411316
add a comment |
I dont know if this is the best or fastest solution but one option is to merge your 2 dataframes then filter based on your conditions then update:
new = df1.merge(df2, on='TERR')
mask = new[((new['State']=='TX') & (new['Policy Number'].str.contains('FSH')))]
df1['Test'] = 0
df1['Test'].update(mask['FSH'])
Policy Number State TERR Test
0 FSH1235456 FL 1 0.00
1 FSH7643643 TX 2 0.15
2 CHH123124 GA 3 0.00
3 CHH123145252 TX 4 0.00
4 CHH124124 TX 5 0.00
answered Nov 19 '18 at 18:39
ChrisChris
1,9411316
I dont know if this is the best or fastest solution but one option is to merge your 2 dataframes then filter based on your conditions then update:
new = df1.merge(df2, on='TERR')
mask = new[((new['State']=='TX') & (new['Policy Number'].str.contains('FSH')))]
df1['Test'] = 0
df1['Test'].update(mask['FSH'])
Policy Number State TERR Test
0 FSH1235456 FL 1 0.00
1 FSH7643643 TX 2 0.15
2 CHH123124 GA 3 0.00
3 CHH123145252 TX 4 0.00
4 CHH124124 TX 5 0.00
answered Nov 19 '18 at 18:39
ChrisChris
1,9411316
answered Nov 19 '18 at 18:39
ChrisChris
1,9411316
answered Nov 19 '18 at 18:39
ChrisChris
1,9411316
answered Nov 19 '18 at 18:39
ChrisChris
1,9411316
1,9411316
add a comment |
add a comment |
You can use numpy where by passing conditions,
cond1 = (df1['State'] == 'TX')
cond2 = (df1['Policy Number'].str.contains('FSH'))
cond3 = (df1["TERR"] == df2['TERR'])
df1['Test'] = np.where(cond1 & cond2 & cond3, df2['FSH'], 0)
Policy Number State TERR Test
0 FSH1235456 FL 1 0.00
1 FSH7643643 TX 2 0.15
2 CHH123124 GA 3 0.00
3 CHH123145252 TX 4 0.00
4 CHH124124 TX 5 0.00
answered Nov 19 '18 at 19:23
VaishaliVaishali
18k31028
This is a great solution, however how would you take into consideration the case where the shapes do not match exactly?
– Bjc51192
Nov 19 '18 at 20:32
You can use map when the shapes of dataframes do not match but in this case, the mapping is possible only based TERR columns. The other two conditions are still index based
– Vaishali
Nov 19 '18 at 20:37
add a comment |
You can use numpy where by passing conditions,
cond1 = (df1['State'] == 'TX')
cond2 = (df1['Policy Number'].str.contains('FSH'))
cond3 = (df1["TERR"] == df2['TERR'])
df1['Test'] = np.where(cond1 & cond2 & cond3, df2['FSH'], 0)
Policy Number State TERR Test
0 FSH1235456 FL 1 0.00
1 FSH7643643 TX 2 0.15
2 CHH123124 GA 3 0.00
3 CHH123145252 TX 4 0.00
4 CHH124124 TX 5 0.00
answered Nov 19 '18 at 19:23
VaishaliVaishali
18k31028
This is a great solution, however how would you take into consideration the case where the shapes do not match exactly?
– Bjc51192
Nov 19 '18 at 20:32
You can use map when the shapes of dataframes do not match but in this case, the mapping is possible only based TERR columns. The other two conditions are still index based
– Vaishali
Nov 19 '18 at 20:37
add a comment |
You can use numpy where by passing conditions,
cond1 = (df1['State'] == 'TX')
cond2 = (df1['Policy Number'].str.contains('FSH'))
cond3 = (df1["TERR"] == df2['TERR'])
df1['Test'] = np.where(cond1 & cond2 & cond3, df2['FSH'], 0)
Policy Number State TERR Test
0 FSH1235456 FL 1 0.00
1 FSH7643643 TX 2 0.15
2 CHH123124 GA 3 0.00
3 CHH123145252 TX 4 0.00
4 CHH124124 TX 5 0.00
answered Nov 19 '18 at 19:23
VaishaliVaishali
18k31028
You can use numpy where by passing conditions,
cond1 = (df1['State'] == 'TX')
cond2 = (df1['Policy Number'].str.contains('FSH'))
cond3 = (df1["TERR"] == df2['TERR'])
df1['Test'] = np.where(cond1 & cond2 & cond3, df2['FSH'], 0)
Policy Number State TERR Test
0 FSH1235456 FL 1 0.00
1 FSH7643643 TX 2 0.15
2 CHH123124 GA 3 0.00
3 CHH123145252 TX 4 0.00
4 CHH124124 TX 5 0.00
answered Nov 19 '18 at 19:23
VaishaliVaishali
18k31028
answered Nov 19 '18 at 19:23
VaishaliVaishali
18k31028
answered Nov 19 '18 at 19:23
VaishaliVaishali
18k31028
answered Nov 19 '18 at 19:23
VaishaliVaishali
18k31028
18k31028
This is a great solution, however how would you take into consideration the case where the shapes do not match exactly?
– Bjc51192
Nov 19 '18 at 20:32
You can use map when the shapes of dataframes do not match but in this case, the mapping is possible only based TERR columns. The other two conditions are still index based
– Vaishali
Nov 19 '18 at 20:37
add a comment |
This is a great solution, however how would you take into consideration the case where the shapes do not match exactly?
– Bjc51192
Nov 19 '18 at 20:32
You can use map when the shapes of dataframes do not match but in this case, the mapping is possible only based TERR columns. The other two conditions are still index based
– Vaishali
Nov 19 '18 at 20:37
This is a great solution, however how would you take into consideration the case where the shapes do not match exactly?
– Bjc51192
Nov 19 '18 at 20:32
This is a great solution, however how would you take into consideration the case where the shapes do not match exactly?
– Bjc51192
Nov 19 '18 at 20:32
You can use map when the shapes of dataframes do not match but in this case, the mapping is possible only based TERR columns. The other two conditions are still index based
– Vaishali
Nov 19 '18 at 20:37
You can use map when the shapes of dataframes do not match but in this case, the mapping is possible only based TERR columns. The other two conditions are still index based
– Vaishali
Nov 19 '18 at 20:37
add a comment |
Are you just trying to get the data from df2 into df1? If so, you could reshape df2 using melt and then do a merge.
df1['policy_prefix'] = df1['Policy Number'].str[:3]
df2 = df2.melt(id_vars='TERR', value_vars=['CHH', 'FSH'],
value_name='Test',
var_name='policy_prefix')
df1 = df1.merge(df2, on=['policy_prefix', 'TERR'])
if you only want this to apply to rows where the state is 'TX' then you could set the other values to null after the merge:
import numpy as np
df1.loc[df1.State!='TX', 'Test'] = np.nan
answered Nov 19 '18 at 18:40
RobertRobert
33429
add a comment |
Are you just trying to get the data from df2 into df1? If so, you could reshape df2 using melt and then do a merge.
df1['policy_prefix'] = df1['Policy Number'].str[:3]
df2 = df2.melt(id_vars='TERR', value_vars=['CHH', 'FSH'],
value_name='Test',
var_name='policy_prefix')
df1 = df1.merge(df2, on=['policy_prefix', 'TERR'])
if you only want this to apply to rows where the state is 'TX' then you could set the other values to null after the merge:
import numpy as np
df1.loc[df1.State!='TX', 'Test'] = np.nan
answered Nov 19 '18 at 18:40
RobertRobert
33429
add a comment |
Are you just trying to get the data from df2 into df1? If so, you could reshape df2 using melt and then do a merge.
df1['policy_prefix'] = df1['Policy Number'].str[:3]
df2 = df2.melt(id_vars='TERR', value_vars=['CHH', 'FSH'],
value_name='Test',
var_name='policy_prefix')
df1 = df1.merge(df2, on=['policy_prefix', 'TERR'])
if you only want this to apply to rows where the state is 'TX' then you could set the other values to null after the merge:
import numpy as np
df1.loc[df1.State!='TX', 'Test'] = np.nan
answered Nov 19 '18 at 18:40
RobertRobert
33429
Are you just trying to get the data from df2 into df1? If so, you could reshape df2 using melt and then do a merge.
df1['policy_prefix'] = df1['Policy Number'].str[:3]
df2 = df2.melt(id_vars='TERR', value_vars=['CHH', 'FSH'],
value_name='Test',
var_name='policy_prefix')
df1 = df1.merge(df2, on=['policy_prefix', 'TERR'])
if you only want this to apply to rows where the state is 'TX' then you could set the other values to null after the merge:
import numpy as np
df1.loc[df1.State!='TX', 'Test'] = np.nan
answered Nov 19 '18 at 18:40
RobertRobert
33429
edited Nov 19 '18 at 18:45
answered Nov 19 '18 at 18:40
RobertRobert
33429
answered Nov 19 '18 at 18:40
RobertRobert
33429
answered Nov 19 '18 at 18:40
RobertRobert
33429
33429
add a comment |
add a comment |
Here is your solution:
# ... initialize df1 and df2 here
df3 = df1.join(df2.FSH) # Merge df1 and df2 into a single dataframe
df3 = df3.rename({"FSH": "TEST"}, axis=1) # Change column name
def set_tx_fsh(row):
if row.State == "TX" and "FSH" in row["Policy Number"]:
return row.TEST
else:
return 0
df3.TEST = df3.apply(set_tx_fsh, axis=1) # Set values in "TEST" column based on your condition
answered Nov 19 '18 at 18:47
jadelordjadelord
493511
add a comment |
Here is your solution:
# ... initialize df1 and df2 here
df3 = df1.join(df2.FSH) # Merge df1 and df2 into a single dataframe
df3 = df3.rename({"FSH": "TEST"}, axis=1) # Change column name
def set_tx_fsh(row):
if row.State == "TX" and "FSH" in row["Policy Number"]:
return row.TEST
else:
return 0
df3.TEST = df3.apply(set_tx_fsh, axis=1) # Set values in "TEST" column based on your condition
answered Nov 19 '18 at 18:47
jadelordjadelord
493511
add a comment |
Here is your solution:
# ... initialize df1 and df2 here
df3 = df1.join(df2.FSH) # Merge df1 and df2 into a single dataframe
df3 = df3.rename({"FSH": "TEST"}, axis=1) # Change column name
def set_tx_fsh(row):
if row.State == "TX" and "FSH" in row["Policy Number"]:
return row.TEST
else:
return 0
df3.TEST = df3.apply(set_tx_fsh, axis=1) # Set values in "TEST" column based on your condition
answered Nov 19 '18 at 18:47
jadelordjadelord
493511
Here is your solution:
# ... initialize df1 and df2 here
df3 = df1.join(df2.FSH) # Merge df1 and df2 into a single dataframe
df3 = df3.rename({"FSH": "TEST"}, axis=1) # Change column name
def set_tx_fsh(row):
if row.State == "TX" and "FSH" in row["Policy Number"]:
return row.TEST
else:
return 0
df3.TEST = df3.apply(set_tx_fsh, axis=1) # Set values in "TEST" column based on your condition
answered Nov 19 '18 at 18:47
jadelordjadelord
493511
answered Nov 19 '18 at 18:47
jadelordjadelord
493511
answered Nov 19 '18 at 18:47
jadelordjadelord
493511
answered Nov 19 '18 at 18:47
jadelordjadelord
493511
493511
add a comment |
add a comment |
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