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
5 Answers
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
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
Using "groupby" and list comprehension:
Storing all the split dataframe in list variable and accessing each of the seprated dataframe by their index.
DF = pd.DataFrame({'chr':["chr3","chr3","chr7","chr6","chr1"],'pos':[10,20,30,40,50],})
ans = [y for x, y in DF.groupby('chr', as_index=False)]
accessing the separated DF like this:
ans[0]
ans[1]
ans[len(ans)-1] # this is the last separated DF
accessing the column value of the separated DF like this:
ansI_chr=ans[i].chr
One-liner using the walrus operator (Python 3.8):
df1, df2 = df[(mask:=df['Sales'] >= 30)], df[~mask]
Consider using copy to avoid SettingWithCopyWarning:
df1, df2 = df[(mask:=df['Sales'] >= 30)].copy(), df[~mask].copy()
Alternatively, you can use the method query:
df1, df2 = df.query('Sales >= 30').copy(), df.query('Sales < 30').copy()
I like to use this for speeding up searches or rolling average finds .apply(lambda x...) type functions so I split big files into dictionaries of dataframes:
df_dict = {sale_v: df[df['Sales'] == sale_v] for sale_v in df.Sales.unique()}
This should do it if you wanted to go based on categorical groups.
