Pandas - Add New Column to Dataframe from 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

Add a new column to pandas data frame from dictionary

Construct the Series first:

df["industry"] = pd.Series(d)

Note: This assumes that the dict keys are in the DataFrame index:

In [11]: df
Out[11]:
       2015-12-01
KO    2144.499950
AAPL  5162.959824

In [12]: df["industry"] = pd.Series(d)

In [13]: df
Out[13]:
       2015-12-01                industry
KO    2144.499950  Consumer, Non-cyclical
AAPL  5162.959824              Technology

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)

Pandas - Add Columns to a DataFrame Based in Dict from one of the Columns

You just need a slight change in your comprehension to extract that data.

It should be:

df_times["rodada"] = [d.get('rodada') for d in
df_times["pontos"]]

You want the values of the dictionary key 'rodada' to be the basis of your new column. So you iterate over those dictionary entries in the loop- in other words, d, and then extract the value by key to make the new column.

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

create a dataframe from a dictionary using the keys as a new column?

Use list comprehension for list of dictionaries with concat helper dict for FRUIT column by keys of outer input dicts:

fruit_dict = {
"apple": [{
        "price": 19,
        "store": "xyz"
    },
    {
        "price": 13,
        "store": "abc"
    }
],
"pear": [{
    "price": 25,
    "store": "xyz"
}]}
    

---

df = pd.DataFrame([{**{'FRUIT':k}, **x} for k, v in fruit_dict.items() for x in v])
print (df)
   FRUIT  price store
0  apple     19   xyz
1  apple     13   abc
2   pear     25   xyz

---

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