CODEWITHSUNDEEP
pythonpandas
Haalanam
Haalanam

Reputation: 89

Create a new Column based on the latest column with a value on a dataframe - Pandas

I have a dataframe that looks like this:

orderID         m1              m2              m3      
1       2020-03-04      2020-03-04             NaT
2       2020-03-08             NaT             NaT

And i want to create a new column that displays the latest milestone (mn) available for an Order.

The output would look like something like this

orderID         m1              m2              m3          last_m_available
1       2020-03-04      2020-03-04             NaT                        m2
2       2020-03-08             NaT             NaT                        m1

How would i do this with python ?

Upvotes: 0

Views: 49

Answers (2)

alkylketone
alkylketone

Reputation: 1

You can use dataframe.dropna() to get rid of empty columns for your list. 

cols = df.dropna().columns
df['last_m_available'] = cols
print(df)

                m1         m2  m3 last_m_available
orderID                                           
1       2020-03-04 2020-03-04 NaN               m2
2       2020-03-08        NaN NaN               m1

Upvotes: 0

jezrael
jezrael

Reputation: 862701

You can swap order of columns, test not missing values and use DataFrame.idxmax:

#if orderID is not index
df = df.set_index('orderID')

df = df.apply(pd.to_datetime)
df['last_m_available'] = df.iloc[:, ::-1].notna().idxmax(axis=1)
print (df)
                m1         m2  m3 last_m_available
orderID                                           
1       2020-03-04 2020-03-04 NaT               m2
2       2020-03-08        NaT NaT               m1

If possible some rows with only missing values:

df = df.apply(pd.to_datetime)
mask = df.iloc[:, ::-1].notna()
df['last_m_available'] = np.where(mask.any(axis=1), mask.idxmax(axis=1), np.nan)
print (df)
                m1         m2  m3 last_m_available
orderID                                           
1       2020-03-04 2020-03-04 NaT               m2
2              NaT        NaT NaT              NaN

Upvotes: 2

Related Questions