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python常见函数积累
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shape()
返回数组或者数据框有多少行或者多少列
import numpy as np
x = np.array([[1,2,5],[2,3,5],[3,4,5],[2,3,6]])
#输出数组的行和列数
print x.shape #结果： (4, 3)
#只输出行数
print x.shape[0] #结果： 4
#只输出列数
print x.shape[1] #结果： 3
因此可以用来遍历行或者列
#计算每列的均值
ex=np.array(np.mean(x[:,i]) for i in range(x.shape[1]))
reshpae()
reshape()是数组array中的方法，作用是将数据重新组织
a = np.array([[1,2,3,4],[5,6,7,8]]) #二维数组
print(a.shape[0]) #值为2，最外层矩阵有2个元素，2个元素还是矩阵。
print(a.shape[1]) #值为4，内层矩阵有4个元素。
b= np.array([1,2,3,4,5,6,7,8])
b.reshape(2,4)
print(b)
#array([[1,2,3,4],
[5,6,7,8]])
pd.Dataframe.columns
返回数据框的列名
pd.Dataframe.columns.values
返回数据框的的列值
[[]]
我之前想提取两列，哈哈，想半天，最后看了一个同学给的demo
直接pd.[["列名","列名"]]
还是见的太少了
_
就是常见的命名规则，
这里指代损失函数
# Create centroids with kmeans for 2 clusters
cluster_centers,_ = kmeans(fifa[scaled_features], 2)
unique（）
去重函数，默认是行去重
[]
# Leave this list as is
number_cols = ['HP', 'Attack', 'Defense']
# Remove the feature without variance from this list
non_number_cols = ['Name', 'Type', 'Legendary']
# Create a new dataframe by subselecting the chosen features
df_selected = pokemon_df[number_cols + non_number_cols]
<script.py> output:
HP Attack Defense Name Type Legendary
0 45 49 49 Bulbasaur Grass False
1 60 62 63 Ivysaur Grass False
2 80 82 83 Venusaur Grass False
3 80 100 123 VenusaurMega Venusaur Grass False
4 39 52 43 Charmander Fire False
# Leave this list as is
number_cols = ['HP', 'Attack', 'Defense']
# Remove the feature without variance from this list
non_number_cols = ['Name', 'Type', 'Legendary']
# Create a new dataframe by subselecting the chosen features
df_selected = pokemon_df[number_cols + non_number_cols]
<script.py> output:
HP Attack Defense Name Type Legendary
0 45 49 49 Bulbasaur Grass False
1 60 62 63 Ivysaur Grass False
2 80 82 83 Venusaur Grass False
3 80 100 123 VenusaurMega Venusaur Grass False
4 39 52 43 Charmander Fire False
比如这个栗子，可以用来提取子数据框
format
print("{} rows in test set vs. {} in training set. {} Features.".format(X_test.shape[0], X_train.shape[0], X_test.shape[1]))
print("{} rows in test set vs. {} in training set. {} Features.".format(X_test.shape[0], X_train.shape[0], X_test.shape[1]))
输出保留一位百分比小数的结果
print("{0:.1%} accuracy on test set.".format(acc))
isnull()
判断是否有缺失值
返回bool
.sum()
除了求和之外还有判断个数此时等同于count
pd.isnull.sum()
.dtypes
DataFrame.dtypes
返回DataFrame中的dtypes
这将返回一个Series，其中包含每列的数据类型。结果的索引是原始DataFrame的列。具有混合类型的列与objectdtype 一起存储
1.type() 返回参数的数据类型
2.dtype 返回数组中元素的数据类型
3.astype() 对数据类型进行转换
value_counts()
value_counts()是一种查看表格某列中有多少个不同值的快捷方法，并计算每个不同值有在该列中有多少重复值。
所以就是统计
In [3]: volunteer["category_desc"].value_counts()
Out[3]:
Strengthening Communities 307
Helping Neighbors in Need 119
Education 92
Health 52
Environment 32
Emergency Preparedness 15
Name: category_desc, dtype: int64
apply
我先放个栗子，后面继续补充这个函数，感觉做一些简单的处理很好用
# Create a list of the columns to average
run_columns = ["run1", "run2", "run3", "run4", "run5"]
# Use apply to create a mean column
running_times_5k["mean"] = running_times_5k.apply(lambda row: row[run_columns].mean(), axis=1)
# Take a look at the results
print(running_times_5k)
script.py> output:
name run1 run2 run3 run4 run5 mean
0 Sue 20.1 18.5 19.6 20.3 18.3 19.36
1 Mark 16.5 17.1 16.9 17.6 17.3 17.08
2 Sean 23.5 25.1 25.2 24.6 23.9 24.46
3 Erin 21.7 21.1 20.9 22.1 22.2 21.60
4 Jenny 25.8 27.1 26.1 26.7 26.9 26.52
5 Russell 30.9 29.6 31.4 30.4 29.9 30.44
