Shulou(Shulou.com)06/03 Report--

The function of Pandas applies apply and applymap1. Functions that can be used directly with NumPy

Sample code:

Df = pd.DataFrame (np.random.randn (5,4)-1) print (df) print (np.abs (df))

Running result:

0 12 30-0.062413 0.844813-1.853721-1.9807171-0.539628-1.975173-0.856597-2.6124062-1.277081-1.088457-0.152189 0.5303253-1.356578-1.996441 0.368822-2.2114784-0.562777 0.518648-2.007223 0.059411 01230 0.062413 0.844813 1.853721 1.9807171 0.539628 1.975173 0.856597 2.6124062 1.277081 1.088457 0.152189 0.5303253 1.356578 1.996441 0.368822 2.2114784 0.562777 0.518648 2.007223 0.0594112. Apply a function to a column or row through apply

Sample code:

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

Running result:

0-0.0624131 0.8448132 0.3688223 0.530325dtype: float64 pay attention to the direction of the specified axis. Default axis=0, direction is column # specify axis direction, axis=1, direction is row print (lambda x: x.max (), axis=1) ```python0-0.0624131 0.8448132 0.3688223 0.530325dtype: float643. Apply a function to each data through applymap

Sample code:

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

Running result:

01 230-0.06 0.84-1.85-1.981-0.54-1.98-0.86-2.612-1.28-1.09-0.15 0.533-1.36-2.00 0.37-2.214-0.56 0.52-2.01 0.06 sort 1. Index sort sort_index ()

Sort uses ascending sort by default, and ascending=False uses descending sort.

Sample code:

# Seriess4 = pd.Series (range (10,15), index = np.random.randint (5, size=5)) print (S4) # Index sort s4.sort_index () # 0 0 1 3 3

Running result:

0 103 111 123 130 14dtype: int640 100 141 123 113 13dtype: int64 pay attention to the axis direction when operating on DataFrame

Sample code:

# DataFramedf4 = 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

Running result:

14 0 1 22-0.416686-0.161256 0.088802-0.004294 1.1641381-0.671914 0.531256 0.303222-0.509493-0.3425731 1.988321-0.466987 2.787891-1.105912 0.889082 1 02-0.161256 1.164138-0.416686-0.004294 0.0888021 0.531256 -0.342573-0.671914-0.509493 0.3032221-0.466987 0.889082 1.988321-1.105912 2.7878912. Sort sort_values by value (by='column name')

Sort by a unique column name, and an error is reported if there are other identical column names.

Sample code:

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

Running result:

14 0 1 21 1.988321-0.466987 2.787891-1.105912 0.8890821-0.671914 0.531256 0.303222-0.509493-0.3425732-0.416686-0.161256 0.088802-0.004294 1.164138 deal with missing data

Sample 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 ())

Running result:

01 20-0.281885-0.786572 0.4871261 1.000000 2.000000 NaN2 NaN 4.000000 NaN3 1.000000 2.000000 3.0000001. Determine if there is a missing value: isnull ()

Sample code:

# isnullprint (df_data.isnull ())

Running result:

0 1 20 False False False1 False False True2 True False True3 False False False2. Discard missing data: dropna () discards rows or columns containing NaN based on the direction of the axis axis. Sample code: # dropnaprint (df_data.dropna ()) print (df_data.dropna (axis=1))

Running result:

0 1 20-0.281885-0.786572 0.4871263 1.000000 2.000000 3.000000 10-0.7865721 2.0000002 4.0000003 2.0000003. Populate missing data: fillna ()

Sample code:

# fillnaprint (df_data.fillna (- 100.)

Running result:

0 1 20-0.281885-0.786572 0.4871261 1.000000 2.000000-100.0000002-100.000000 4.000000-100.0000003 1.000000 2.000000 3.000000

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