pandas简单教程1
Series
import pandas as pd
import numpy as np
s = pd.Series([1,3,6,np.nan,44,1])print(s)
"""
0 1.0
1 3.0
2 6.0
3 NaN
4 44.0
5 1.0
dtype: float64
"""Series的字符串表现形式为：索引在左边，值在右边。由于我们没有为数据指定索引。于是会自动创建一个0到N-1（N为长度）的整数型索引。
DataFrame
dates = pd.date_range('20160101',periods=6)
df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','d'])print(df)
"""a b c d
2016-01-01 -0.253065 -2.071051 -0.640515 0.613663
2016-01-02 -1.147178 1.532470 0.989255 -0.499761
2016-01-03 1.221656 -2.390171 1.862914 0.778070
2016-01-04 1.473877 -0.046419 0.610046 0.204672
2016-01-05 -1.584752 -0.700592 1.487264 -1.778293
2016-01-06 0.633675 -1.414157 -0.277066 -0.442545
"""DataFrame是一个表格型的数据结构，它包含有一组有序的列，每列可以是不同的值类型（数值，字符串，布尔值等）。DataFrame既有行索引也有列索引， 它可以被看做由Series组成的大字典。
我们可以根据每一个不同的索引来挑选数据, 比如挑选 b 的元素:
DataFrame 的一些简单运用
print(df['b'])"""
2016-01-01 -2.071051
2016-01-02 1.532470
2016-01-03 -2.390171
2016-01-04 -0.046419
2016-01-05 -0.700592
2016-01-06 -1.414157
Freq: D, Name: b, dtype: float64
"""我们在创建一组没有给定行标签和列标签的数据 df1:
df1 = pd.DataFrame(np.arange(12).reshape((3,4)))
print(df1)"""0 1 2 3
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
"""这样,他就会采取默认的从0开始 index. 还有一种生成 df 的方法, 如下 df2:
df2 = pd.DataFrame({'A' : 1.,'B' : pd.Timestamp('20130102'),'C' : pd.Series(1,index=list(range(4)),dtype='float32'),'D' : np.array([3] * 4,dtype='int32'),'E' : pd.Categorical(["test","train","test","train"]),'F' : 'foo'})print(df2)"""A B C D E F
0 1.0 2013-01-02 1.0 3 test foo
1 1.0 2013-01-02 1.0 3 train foo
2 1.0 2013-01-02 1.0 3 test foo
3 1.0 2013-01-02 1.0 3 train foo
"""这种方法能对每一列的数据进行特殊对待. 如果想要查看数据中的类型, 我们可以用 dtype 这个属性:
print(df2.dtypes)"""
df2.dtypes
A float64
B datetime64[ns]
C float32
D int32
E category
F object
dtype: object
"""如果想看对列的序号:
print(df2.index)# Int64Index([0, 1, 2, 3], dtype='int64')同样, 每种数据的名称也能看到:
print(df2.columns)# Index(['A', 'B', 'C', 'D', 'E', 'F'], dtype='object')如果只想看所有df2的值:
print(df2.values)"""
array([[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'test', 'foo'],[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'train', 'foo'],[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'test', 'foo'],[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'train', 'foo']], dtype=object)
"""想知道数据的总结, 可以用 describe():
df2.describe()"""A C D
count 4.0 4.0 4.0
mean 1.0 1.0 3.0
std 0.0 0.0 0.0
min 1.0 1.0 3.0
25% 1.0 1.0 3.0
50% 1.0 1.0 3.0
75% 1.0 1.0 3.0
max 1.0 1.0 3.0
"""如果想翻转数据, transpose:
print(df2.T)"""
0 1 2 \
A 1 1 1
B 2013-01-02 00:00:00 2013-01-02 00:00:00 2013-01-02 00:00:00
C 1 1 1
D 3 3 3
E test train test
F foo foo foo 3
A 1
B 2013-01-02 00:00:00
C 1
D 3
E train
F foo """如果想对数据的 index 进行排序并输出:
print(df2.sort_index(axis=1, ascending=False))"""F E D C B A
0 foo test 3 1.0 2013-01-02 1.0
1 foo train 3 1.0 2013-01-02 1.0
2 foo test 3 1.0 2013-01-02 1.0
3 foo train 3 1.0 2013-01-02 1.0
"""如果是对数据 值 排序输出:
print(df2.sort_values(by='B'))"""A B C D E F
0 1.0 2013-01-02 1.0 3 test foo
1 1.0 2013-01-02 1.0 3 train foo
2 1.0 2013-01-02 1.0 3 test foo
3 1.0 2013-01-02 1.0 3 train foo
"""实验代码：
import pandas as pd
import numpy as npif __name__ == '__main__':s = pd.Series([1, 2, 3], index=['a', 'b', 'c'])print(s)dates = pd.date_range('20200826', periods=6)df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=['a', 'b', 'c', 'd'])print(df)# 查看某一列print(df['b'])df1 = pd.DataFrame(np.arange(12).reshape(3, 4))print(df1)df2 = pd.DataFrame({'A': 1.,'B': pd.Timestamp('20130102'),'C': pd.Series(1, index=list(range(4)), dtype='float32'),'D': np.array([3] * 4, dtype='int32'),'E': pd.Categorical(["test", "train", "test", "train"]),'F': 'foo'})print(df2)print(df2.dtypes)print(df2.index)print(df2.columns)print(df2.values)print(df2.describe())print(df2.T)print(df2.sort_index(axis=1, ascending=False))