Pandas入门学习之一 Series和DataFrame
2019-03-10 10:51
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参考:https://www.jianshu.com/u/ff0242a57145
import pandas as pd import numpy as np # 1. 合理应用 dir 和help # print(dir(pd)) # print(help(pd.Series)) # 2. Series # 2.1 list创建 S_with_user_label = pd.Series([1,2,3,4,5],['a','b','c','a','b']) S_with_default_label = pd.Series([1,2,3,4,5]) print(S_with_default_label) print(S_with_user_label) # 2.2 dict创建,无index按键来创建,有index按index创建 S_with_user_label_1 = pd.Series({'a': 1, 'b': 2, 'c': 3}) print(S_with_user_label_1) # 2.3 scalar创建相同值 S_from_scalar = pd.Series(5, ['a', 'b', 'c']) print(S_from_scalar) # 2.4 operation # 2.4.1 slice random_data = np.random.rand(1, 5) # np.random.rand(5)区别 print(random_data[0]) print(random_data[0, 4]) Series = pd.Series(random_data[0], index=['A', 'B', 'C', 'D', 'E']) print(Series) print(Series[1]) print(Series[1:3]) print("Max is %f, Min is %f, Median is %f" % (Series.max(), Series.min(), Series.median())) print(Series[Series > Series.mean()]) # 取大于平均值的值 print(Series[[3, 1]]) # 索引为list 按list迭代查询 # 2.4.2 作为Numpy函数的输入参数 print(np.exp(Series)) print(np.sin(Series)) # 2.4.3 Dict 利用键值对来引用 print(Series['A']) # 利用label查看 Series['G'] = 10 # 修改 有则改无则添 print('B' in Series) Series.at['F'] = -5 # 添加 print(Series) # 2.4.4 + - * / 只有标签相同才可以 print(Series+Series) print(Series*3) # 3 DataFrame index columns values # 3.1 创建dict + series Data = {"A": pd.Series([1, 2, 3], index=['a', 'b', 'c']), "B": [4, 5, 6], "C": [7, 8, 9]} DataFra = pd.DataFrame(Data) print(Data) print(DataFra) print(DataFra.index, DataFra.columns, DataFra.values) # 3.2 创建dict + index Data1 = {"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]} DataFra_1 = pd.DataFrame(Data1, index=['A1', 'B1', 'C1']) print(DataFra_1) # 3.3 创建 np.ndarray + index Data2 = np.zeros((3,), dtype=[("A1", "f4"), ("B1", "f4"), ("C1", "U10")]) Data2[:] = [(1, 2, 'hello'), (3, 4, 'world'), (5, 6, 'ly')] DataFra_2 = pd.DataFrame(Data2, index=['AA1', 'BB1', 'CC1']) print(DataFra_2 ) # 3.4 理解 i4 f8 c10 a25 u25 dt = np.dtype('i4') # 查 # 3.5 基本操作 print(DataFra_2['A1']) # index列索引 无columns行索引 DataFra_2['D1'] = [7, 8, 'liu'] # 添 DataFra_2.insert(2, 'AA', [11, 22, 33]) #添 print(DataFra_2) del DataFra_2['D1'] # 无返回值 print(DataFra_2) A =DataFra_2.pop('AA') # 有返回值 print(DataFra_2) print(DataFra_2.loc['AA1']) #按行键索引 print(DataFra_2.iloc[1]) #按行序号索引 DataFra_2.drop(index=['CC1'], inplace=True) #删除C1列 print(DataFra_2) DataFra_2.loc['C1']=[111, 222, '333'] print(DataFra_2) # 3.6 常用属性、方法和运算 .csv文件 data_new = pd.read_csv('iris.csv') print(data_new.describe()) print(data_new.head(3)) print(data_new.tail(3))
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