Insert value into a dataframe column based on condition
How do I rewrite the last two lines of the below code, so that the last line does not overwrite the second to last line?
Desired result is that the "color" column will have either "pink" or "orange" values put in depending on which condition is met: "KOM" or "Top 10".
import pandas as pd
import numpy as np
def contains_BO(seg_effs):
# check if segment efforts for activity contain any best overall effort
for eff in seg_effs:
rank = eff['kom_rank']
if rank != None:
if rank == 1:
return "KOM"
else:
return "Top 10"
activities = pd.read_pickle('strava.pk1')
activities['color'] = np.where(activities['segment_efforts'].map(contains_BO) == 'KOM', "orange", "grey")
activities['color'] = np.where(activities['segment_efforts'].map(contains_BO) == 'Top 10', "pink", "grey")
python pandas numpy
asked Nov 11 at 21:32
barciewicz
515312
add a comment |
How do I rewrite the last two lines of the below code, so that the last line does not overwrite the second to last line?
Desired result is that the "color" column will have either "pink" or "orange" values put in depending on which condition is met: "KOM" or "Top 10".
import pandas as pd
import numpy as np
def contains_BO(seg_effs):
# check if segment efforts for activity contain any best overall effort
for eff in seg_effs:
rank = eff['kom_rank']
if rank != None:
if rank == 1:
return "KOM"
else:
return "Top 10"
activities = pd.read_pickle('strava.pk1')
activities['color'] = np.where(activities['segment_efforts'].map(contains_BO) == 'KOM', "orange", "grey")
activities['color'] = np.where(activities['segment_efforts'].map(contains_BO) == 'Top 10', "pink", "grey")
python pandas numpy
asked Nov 11 at 21:32
barciewicz
515312
add a comment |
How do I rewrite the last two lines of the below code, so that the last line does not overwrite the second to last line?
Desired result is that the "color" column will have either "pink" or "orange" values put in depending on which condition is met: "KOM" or "Top 10".
import pandas as pd
import numpy as np
def contains_BO(seg_effs):
# check if segment efforts for activity contain any best overall effort
for eff in seg_effs:
rank = eff['kom_rank']
if rank != None:
if rank == 1:
return "KOM"
else:
return "Top 10"
activities = pd.read_pickle('strava.pk1')
activities['color'] = np.where(activities['segment_efforts'].map(contains_BO) == 'KOM', "orange", "grey")
activities['color'] = np.where(activities['segment_efforts'].map(contains_BO) == 'Top 10', "pink", "grey")
python pandas numpy
asked Nov 11 at 21:32
barciewicz
515312
How do I rewrite the last two lines of the below code, so that the last line does not overwrite the second to last line?
Desired result is that the "color" column will have either "pink" or "orange" values put in depending on which condition is met: "KOM" or "Top 10".
import pandas as pd
import numpy as np
def contains_BO(seg_effs):
# check if segment efforts for activity contain any best overall effort
for eff in seg_effs:
rank = eff['kom_rank']
if rank != None:
if rank == 1:
return "KOM"
else:
return "Top 10"
activities = pd.read_pickle('strava.pk1')
activities['color'] = np.where(activities['segment_efforts'].map(contains_BO) == 'KOM', "orange", "grey")
activities['color'] = np.where(activities['segment_efforts'].map(contains_BO) == 'Top 10', "pink", "grey")
python pandas numpy
python pandas numpy
asked Nov 11 at 21:32
barciewicz
515312
asked Nov 11 at 21:32
barciewicz
515312
asked Nov 11 at 21:32
barciewicz
515312
asked Nov 11 at 21:32
barciewicz
515312
asked Nov 11 at 21:32
barciewicz
515312
515312
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
You use something like this:
import pandas as pd
df = pd.DataFrame({"a": range(4), "b": ["x", "x", "y", "y"]})
df
a b
0 0 x
1 1 x
2 2 y
3 3 y
# assign 5 to rows of "a" where "b" == "x"
df.loc[df["b"] == "x", "a"] = 5
df
a b
0 5 x
1 5 x
2 2 y
3 3 y
Alternatively you can create a new column out of a dict of values:
df["val"] = df["b"].map({"x": 5, "y": 6})
df
a b val
0 5 x 5
1 5 x 5
2 2 y 6
3 3 y 6
map also supports functions if you need more complex logic.
answered Nov 11 at 21:53
pawroman
935612
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
You use something like this:
import pandas as pd
df = pd.DataFrame({"a": range(4), "b": ["x", "x", "y", "y"]})
df
a b
0 0 x
1 1 x
2 2 y
3 3 y
# assign 5 to rows of "a" where "b" == "x"
df.loc[df["b"] == "x", "a"] = 5
df
a b
0 5 x
1 5 x
2 2 y
3 3 y
Alternatively you can create a new column out of a dict of values:
df["val"] = df["b"].map({"x": 5, "y": 6})
df
a b val
0 5 x 5
1 5 x 5
2 2 y 6
3 3 y 6
map also supports functions if you need more complex logic.
answered Nov 11 at 21:53
pawroman
935612
add a comment |
You use something like this:
import pandas as pd
df = pd.DataFrame({"a": range(4), "b": ["x", "x", "y", "y"]})
df
a b
0 0 x
1 1 x
2 2 y
3 3 y
# assign 5 to rows of "a" where "b" == "x"
df.loc[df["b"] == "x", "a"] = 5
df
a b
0 5 x
1 5 x
2 2 y
3 3 y
Alternatively you can create a new column out of a dict of values:
df["val"] = df["b"].map({"x": 5, "y": 6})
df
a b val
0 5 x 5
1 5 x 5
2 2 y 6
3 3 y 6
map also supports functions if you need more complex logic.
answered Nov 11 at 21:53
pawroman
935612
add a comment |
You use something like this:
import pandas as pd
df = pd.DataFrame({"a": range(4), "b": ["x", "x", "y", "y"]})
df
a b
0 0 x
1 1 x
2 2 y
3 3 y
# assign 5 to rows of "a" where "b" == "x"
df.loc[df["b"] == "x", "a"] = 5
df
a b
0 5 x
1 5 x
2 2 y
3 3 y
Alternatively you can create a new column out of a dict of values:
df["val"] = df["b"].map({"x": 5, "y": 6})
df
a b val
0 5 x 5
1 5 x 5
2 2 y 6
3 3 y 6
map also supports functions if you need more complex logic.
answered Nov 11 at 21:53
pawroman
935612
You use something like this:
import pandas as pd
df = pd.DataFrame({"a": range(4), "b": ["x", "x", "y", "y"]})
df
a b
0 0 x
1 1 x
2 2 y
3 3 y
# assign 5 to rows of "a" where "b" == "x"
df.loc[df["b"] == "x", "a"] = 5
df
a b
0 5 x
1 5 x
2 2 y
3 3 y
Alternatively you can create a new column out of a dict of values:
df["val"] = df["b"].map({"x": 5, "y": 6})
df
a b val
0 5 x 5
1 5 x 5
2 2 y 6
3 3 y 6
map also supports functions if you need more complex logic.
answered Nov 11 at 21:53
pawroman
935612
edited Nov 11 at 22:03
answered Nov 11 at 21:53
pawroman
935612
answered Nov 11 at 21:53
pawroman
935612
answered Nov 11 at 21:53
pawroman
935612
935612
add a comment |
add a comment |
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