I’m working with the following DataFrame
0 NaN 1 {u'bphigh': u'120', u'bplow': u'70', u'weight'... 2 NaN 3 {u'bphigh': 120, u'bplow': 60, u'weight': u'10... 4 NaN ... 13149 NaN 13150 {u'bphigh': u'110', u'bplow': u'60', u'weight'... 13151 {u'bphigh': u'149', u'bplow': u'90', u'weight'... 13152 {u'bphigh': u'113', u'bplow': u'69', u'weight'... 13153 {u'bphigh': u'115', u'bplow': u'76', u'weight'...
Consisting of parameters (bphigh
bplow
weight
) of type str
as follows
{u'bphigh': u'120', u'bplow': u'70', u'weight': u'84.8'}
I’d like to extract these parameters and their corresponding values to columns as shown below
bphigh bplow weight 0 11 22 31 1 42 52 61 2 72 82 91
I tried using the following pandas method which hasn’t really been consistent in extracting the parameters
vitals['vital'].str.extract(r"{u'bphigh':s*(w+)")
Is there a more efficient workaround in pandas or regex to this issue?
Please Advise
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Answer
from ast import literal_eval
try:
df['vital']=df['vital'].astype(str).map(lambda x:literal_eval(x) if x!='nan' else float('NaN')) #In the above code we are making the string values to actual dictionary via #map() method we are iterating the values of 'vital' column and converting the #string dictionary to actual dictionary via literal_eval() method and anonymous function #skipping 'nan's' via if/else condition inside map() method
Finally:
out=pd.DataFrame(df['vital'].dropna().tolist())[['bphigh','bplow','weight']] #In the code we are making Dataframe out of the dictionary values in #'vital' columns by making list of values of 'vial' column and then we #are selecting only these 3 columns ['bphigh','bplow','weight']
Now If you print out
you will get your desired output