# apply and applymap

### 1. Can be used directly NumPy Function of

Sample code ：

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

Running results ：

``` 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. adopt apply Apply a function to a column or row

Sample code ：

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

Running results ：

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

Note the direction of the specified axis , Default axis=0, The direction is the column

Sample code ：

```# Specify the axis direction ,axis=1, The direction is OK
print(df.apply(lambda x : x.max(), axis=1))```

Running results ：

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

### 3. adopt applymap Apply functions to each data

Sample code ：

```# Use applymap Apply to every data
f2 = lambda x : '%.2f' % x
print(df.applymap(f2))```

Running results ：

``` 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 sort

sort_index()

Sort by default uses ascending sort ,ascending=False Sort in descending order

Sample code ：

```# Series
s4 = 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 results ：

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

Yes DataFrame Pay attention to the axis direction during operation

Sample 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```

Running results ：

``` 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 by a unique column name , If there are other identical column names, an error will be reported .

Sample code ：

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

Running results ：

``` 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```

# Processing missing data

Sample code ：

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

Running results ：

``` 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. Determine if there are missing values ：isnull()

Sample code ：

```# isnull
print(df_data.isnull())```

Running results ：

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

### 2. Discard missing data ：dropna()

according to axis Axis direction , Discard contains NaN Row or column of . Sample code ：

```# dropna
print(df_data.dropna())
print(df_data.dropna(axis=1))```

Running results ：

``` 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()

Sample code ：

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

Running results ：

``` 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```

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