Adding a New Pandas Column With Mapped Value from a Dictionary

pandas - add new column to dataframe from dictionary

Call map and pass the dict, this will perform a lookup and return the associated value for that key:

In [248]:

d = {112: 'en', 113: 'es', 114: 'es', 111: 'en'}
df['D'] = df['U'].map(d)
df
Out[248]:
     U   L   D
0  111  en  en
1  112  en  en
2  112  es  en
3  113  es  es
4  113  ja  es
5  113  zh  es
6  114  es  es

map value of nested dictionary to new column in dataframe in python

Looks like a list comprehension with a double loop should do the job:

df['malicious'] = [d.get('malicious') for outer_d in df['dictionary'] for d in outer_d.values()]

or you can try the following code that creates a DataFrame out of the "dictionary" column and gets the value under the "malicious" key using str.get:

df['malicious'] = (pd.DataFrame(df['dictionary'].tolist()).set_axis([0]*len(df), axis=1)
                   .groupby(level=0, axis=1)
                   .first()[0]
                   .str.get('malicious'))

Output:

                                          dictionary  malicious
0  {'https://twitter.com/tonythehuff/status/92718...      False
1  {'http://giveaway.amazon.com\__CONNECTIONPOOL_...      False
2  {'http://pcktpro.com\__CONNECTIONPOOL_ERROR__'...       True

Pandas Add column based on dict value of another column

This should do the trick, use map:

your_dict={
    "Val1" : "new_val1",
    "Val2" : "new_val2",
    "Val3" : "new_val3",
}

df['col_new'] = df['col1'].map(your_dict)

which prints your desired output:

   col1   col_new
0  Val1  new_val1
1  Val2  new_val2
2  Val3  new_val3
3  Val1  new_val1
4  Val1  new_val1
5  Val1  new_val1

Remap values in pandas column with a dict, preserve NaNs

You can use .replace. For example:

>>> df = pd.DataFrame({'col2': {0: 'a', 1: 2, 2: np.nan}, 'col1': {0: 'w', 1: 1, 2: 2}})
>>> di = {1: "A", 2: "B"}
>>> df
  col1 col2
0    w    a
1    1    2
2    2  NaN
>>> df.replace({"col1": di})
  col1 col2
0    w    a
1    A    2
2    B  NaN

or directly on the Series, i.e. df["col1"].replace(di, inplace=True).

Map dict keys to new column in pandas df

Use DataFrame.drop_duplicates:

df = df.drop_duplicates('Time')

If possible, here is another solution - it match values by Series.map:

#swap key values in dict
#http://stackoverflow.com/a/31674731/2901002
d = {k: oldk for oldk, oldv in hdict.items() for k in oldv}
df["H"] = df['Code'].map(d)
df = df.dropna(subset=['H']).drop_duplicates('Time')
print (df)
                    Time Code  H
0  2019-08-02 09:50:10.1    A  1
2  2019-08-02 09:50:10.2    X  2
3  2019-08-02 09:50:10.3    Y  2
4  2019-08-02 09:50:10.4    A  1
6  2019-08-02 09:50:10.5    X  2
8  2019-08-02 09:50:10.6    Z  2

How to add a column with values of a dictionary in Python

Is this what you want?

df = df.set_index('id')
dictionary = {1:[5,8,6,3], 2:[1,2], 5:[8,6,2]}    
df['new_column'] = pd.Series(dictionary)

Note: The keys of the dictionary need to be the same type (int) as the index of the data frame.

>>> print(df)
    gender    new_column
id                      
1        0  [5, 8, 6, 3]
2        0        [1, 2]
3        1           NaN
4        1           NaN
5        1     [8, 6, 2]

Update:

A better solution if 'id' column contains duplicates (see comments below):

df['new_column'] = df['id'].map(dictionary)

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