Pandas Update and Add Rows One Dataframe With Key Column in Another Dataframe

Pandas update and add rows one dataframe with key column in another dataframe

It seems like you're looking for combine_first.

a = df2.set_index('key')
b = df1.set_index('key')

(a.combine_first(b)
  .reset_index()
  .reindex(columns=df1.columns))

     A    B key    C
0  4.0  5.0  k1  2.0
1  1.0  2.0  k2  3.0
2  2.0  3.0  k3  5.0
3  2.0  3.0  k4  5.0

How to add ROW of one dataframe to column of another dataframe?

You can extract the values from the row, use np.flip() to reverse them and then create a new column.

import numpy as np

df2['CDF'] = np.flip(df1.loc['CDF'].values)

pandas: append rows to another dataframe under the similar row based on column condition

First idea is filter df2 values by df1.col1 and append to df1 by concat and then sorting by DataFrame.sort_values:

df = pd.concat([df1, df2[(df2.col1.isin(df1.col1))]]).sort_values('col1', ignore_index=True)
print (df)
                       col1 col2 col3
0              I ate dinner  min  min
1              I ate dinner  max  max
2              I ate dinner  min  max
3  the play was inetresting  mid  max
4  the play was inetresting  min  max
5  the play was inetresting  max  mid

If need only common values in both DataFrames is possible filter by numpy.intersect1d:

common = np.intersect1d(df1['col1'], df2['col1'])

df = (pd.concat([df1[df1.col1.isin(common)],
                 df2[df2.col1.isin(common)]])
                     .sort_values('col1', ignore_index=True))
print (df)

Updating a dataframe rows based on another dataframe rows

you can use update...like so..

df1.update(df2)

How to update one pandas dataframe with another dataframe (update the old data and add new data)

Use concat with DataFrame.drop_duplicates:

df = pd.concat([df2, df1]).drop_duplicates(subset=['key'])
print (df)
  assignee  id issuetype   key
0      Tom   1     story  TP-1
1     Anna   2       bug  TP-2
2      Tim   3       bug  TP-3
3     Jane   4       bug  TP-4

Or DataFrame.update with DataFrame.reindex:

cols = df1.columns
df1 = df1.set_index('key')
df2 = df2.set_index('key')
df1 = df1.reindex(columns=df1.columns.union(df2.columns, sort=False),
                  index=df1.index.union(df2.index, sort=False))

df1.update(df2)
df1 = df1.reset_index().reindex(columns=cols)
print (df1)

  assignee   id issuetype   key
0      Tom  1.0     story  TP-1
1     Anna  2.0       bug  TP-2
2      Tim  3.0       bug  TP-3
3     Jane  4.0       bug  TP-4

Python Pandas update a dataframe value from another dataframe

You can using concat + drop_duplicates which updates the common rows and adds the new rows in df2

pd.concat([df1,df2]).drop_duplicates(['Code','Name'],keep='last').sort_values('Code')
Out[1280]: 
   Code      Name  Value
0     1  Company1    200
0     2  Company2   1000
2     3  Company3    400

Update due to below comments

df1.set_index(['Code', 'Name'], inplace=True)

df1.update(df2.set_index(['Code', 'Name']))

df1.reset_index(drop=True, inplace=True)

pandas update specific rows in specific columns in one dataframe based on another dataframe

Use DataFrame.update by ID converted to index and selecting columns for processing - here only age and city:

df11 = df1.set_index('ID')
df22 = df2.set_index('ID')[['age','city']]
df11.update(df22)
df = df11.reset_index()
print (df)
   ID   name country       city     hobby   age
0  12   Meli    Peru       Lima    eating  24.0
1  15   Saya     USA   new-york  drinking  34.0
2  34  Aitel  Jordan      Amman    riding  51.0
3  23  Tanya  Russia     Moscow    sports  75.0
4  44    Gil   Spain  Barcelona    paella  21.0

---

Related Topics