Function application of Pandas

Function application of Pandas

apply and applymap

1. Function of NumPy can be used directly

Example code:

# Numpy ufunc function
df = pd.DataFrame(np.random.randn(5,4) - 1)
print(df)

print(np.abs(df))

Operation result:

          0         1         2         3
0 -0.062413  0.844813 -1.853721 -1.980717
1 -0.539628 -1.975173 -0.856597 -2.612406
2 -1.277081 -1.088457 -0.152189  0.530325
3 -1.356578 -1.996441  0.368822 -2.211478
4 -0.562777  0.518648 -2.007223  0.059411

          0         1         2         3
0  0.062413  0.844813  1.853721  1.980717
1  0.539628  1.975173  0.856597  2.612406
2  1.277081  1.088457  0.152189  0.530325
3  1.356578  1.996441  0.368822  2.211478
4  0.562777  0.518648  2.007223  0.059411

2. apply the function to the column or row by applying

Example code:

# Using apply to apply row or column data
#f = lambda x : x.max()
print(df.apply(lambda x : x.max()))

Operation result:

0   -0.062413
1    0.844813
2    0.368822
3    0.530325
dtype: float64

Note that the direction of the specified axis is axis=0 by default, and the direction is column

Example code:

# Specify axis direction, axis=1, direction is line
print(df.apply(lambda x : x.max(), axis=1))

Operation result:

0    0.844813
1   -0.539628
2    0.530325
3    0.368822
4    0.518648
dtype: float64

3. Apply the function to each data through applymap

Example code:

# Apply to each data using applymap
f2 = lambda x : '%.2f' % x
print(df.applymap(f2))

Operation result:

       0      1      2      3
0  -0.06   0.84  -1.85  -1.98
1  -0.54  -1.98  -0.86  -2.61
2  -1.28  -1.09  -0.15   0.53
3  -1.36  -2.00   0.37  -2.21
4  -0.56   0.52  -2.01   0.06

sort

1. Index sorting

sort_index()

Sorting is in ascending order by default, ascending=False is in descending order

Example code:

# Series
s4 = pd.Series(range(10, 15), index = np.random.randint(5, size=5))
print(s4)

# index order
s4.sort_index() # 0 0 1 3 3

Operation result:

0    10
3    11
1    12
3    13
0    14
dtype: int64

0    10
0    14
1    12
3    11
3    13
dtype: int64

Pay attention to the axis direction when operating on DataFrame

Example code:

# DataFrame
df4 = pd.DataFrame(np.random.randn(3, 5), 
                   index=np.random.randint(3, size=3),
                   columns=np.random.randint(5, size=5))
print(df4)

df4_isort = df4.sort_index(axis=1, ascending=False)
print(df4_isort) # 4 2 1 1 0

Operation result:


          1         4         0         1         2
2 -0.416686 -0.161256  0.088802 -0.004294  1.164138
1 -0.671914  0.531256  0.303222 -0.509493 -0.342573
1  1.988321 -0.466987  2.787891 -1.105912  0.889082

          4         2         1         1         0
2 -0.161256  1.164138 -0.416686 -0.004294  0.088802
1  0.531256 -0.342573 -0.671914 -0.509493  0.303222
1 -0.466987  0.889082  1.988321 -1.105912  2.787891

2. Sort by value

sort_values(by='column name')

Sort according to a unique column name. If there are other same column names, an error will be reported.

Example code:

# Ranking by value
df4_vsort = df4.sort_values(by=0, ascending=False)
print(df4_vsort)

Operation result:

          1         4         0         1         2
1  1.988321 -0.466987  2.787891 -1.105912  0.889082
1 -0.671914  0.531256  0.303222 -0.509493 -0.342573
2 -0.416686 -0.161256  0.088802 -0.004294  1.164138

Handling missing data

Example code:

df_data = pd.DataFrame([np.random.randn(3), [1., 2., np.nan],
                       [np.nan, 4., np.nan], [1., 2., 3.]])
print(df_data.head())

Operation result:

          0         1         2
0 -0.281885 -0.786572  0.487126
1  1.000000  2.000000       NaN
2       NaN  4.000000       NaN
3  1.000000  2.000000  3.000000

1. Judge whether there is a missing value: isnull()

Example code:

# isnull
print(df_data.isnull())

Operation result:

       0      1      2
0  False  False  False
1  False  False   True
2   True  False   True
3  False  False  False

2. Discard missing data: dropna()

Discard the row or column containing NaN according to axis direction. Example code:

# dropna
print(df_data.dropna())

print(df_data.dropna(axis=1))

Operation result:

          0         1         2
0 -0.281885 -0.786572  0.487126
3  1.000000  2.000000  3.000000

          1
0 -0.786572
1  2.000000
2  4.000000
3  2.000000

3. Fill in missing data: fillna()

Example code:

# fillna
print(df_data.fillna(-100.))

Operation result:

            0         1           2
0   -0.281885 -0.786572    0.487126
1    1.000000  2.000000 -100.000000
2 -100.000000  4.000000 -100.000000
3    1.000000  2.000000    3.000000

Tags: Lambda

Posted on Thu, 19 Mar 2020 14:21:08 -0400 by itaym02