Pandas split DataFrame by column value

Question

I have DataFrame with column Sales.

How can I split it into 2 based on Sales value?

First DataFrame will have data with 'Sales' < s and second with 'Sales' >= s

Answer 1

You can use boolean indexing:

df = pd.DataFrame({'Sales':[10,20,30,40,50], 'A':[3,4,7,6,1]}) print (df)    A  Sales 0  3     10 1  4     20 2  7     30 3  6     40 4  1     50  s = 30  df1 = df[df['Sales'] >= s] print (df1)    A  Sales 2  7     30 3  6     40 4  1     50  df2 = df[df['Sales'] < s] print (df2)    A  Sales 0  3     10 1  4     20 

It's also possible to invert mask by ~:

mask = df['Sales'] >= s df1 = df[mask] df2 = df[~mask] print (df1)    A  Sales 2  7     30 3  6     40 4  1     50  print (df2)    A  Sales 0  3     10 1  4     20 

---

print (mask) 0    False 1    False 2     True 3     True 4     True Name: Sales, dtype: bool  print (~mask) 0     True 1     True 2    False 3    False 4    False Name: Sales, dtype: bool 

Answer 2

Using groupby you could split into two dataframes like

In [1047]: df1, df2 = [x for _, x in df.groupby(df['Sales'] < 30)]  In [1048]: df1 Out[1048]:    A  Sales 2  7     30 3  6     40 4  1     50  In [1049]: df2 Out[1049]:    A  Sales 0  3     10 1  4     20