How to update multiple entries of a certain column of a Pandas dataframe in certain order?

Question

Let’s say I have the following Pandas dataframe, where the 'key' column only contains unique strings:

import pandas as pd
df = pd.DataFrame({'key':['b','d','c','a','e','f'], 'value': [0,0,0,0,0,0]})
df

  key value
0   b     0
1   d     0
2   c     0
3   a     0
4   e     0
5   f     0

Now I have a list of unique keys and a list of corresponding values:

keys = ['a', 'b', 'c', 'd']
values = [1, 2, 3, 4]

I want to update the 'value' column in the same order of the lists, so that each row has matched 'key' and 'value' (a to 1, 'b' to 2, 'c' to 3, 'd' to 4). I am using the following code, but the dataframe seems to update values from top to bottom, which I don’t quite understand

df.loc[df['key'].isin(keys),'value'] = values
df

  key value
0   b     1
1   d     2
2   c     3
3   a     4
4   e     0
5   f     0

To be clear, I am expecting to get

  key value
0   b     2
1   d     4
2   c     3
3   a     1
4   e     0
5   f     0

Any suggestions?

Asked By: Shaun Han

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Source

Answer 1

keys = ['a', 'b', 'c', 'd']
values = [1, 2, 3, 4]

# form a dictionary with keys and values list
d=dict(zip(keys, values))

# update the value where mapping exists using LOC and MAP 
df.loc[df['key'].map(d).notna(), 'value'] =df['key'].map(d)
df

    key     value
0     b     2
1     d     4
2     c     3
3     a     1
4     e     0
5     f     0

Answered By: Naveed

Answer 2

with a temporary dataframe:

temp_df = df.set_index('key')
temp_df.loc[keys] = np.array(values).reshape(-1, 1)
df = temp_df.reset_index()

Answered By: filippo

Answer 3

Use map:

dd = dict(zip(keys, values))
df['value'] = df['key'].map(dd).fillna(df['value'])

Answered By: Quang Hoang

How to update multiple entries of a certain column of a Pandas dataframe in certain order?

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