Pandas split DataFrame by column value

问题描述：

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

解决方案 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

解决方案 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

解决方案 3：

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')]

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 

解决方案 4：

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()

解决方案 5：

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.