1. append row增加，

1. 增加列

dates = pd.date_range('20121001',periods=10)df = pd.DataFrame(np.random.randn(10,3) , index = dates,columns=list('abc')) df['d'] = pd.Series(np.random.randn(10),index=df.index)

for index, row in data.iterrows():   # 获取每行的index、row        for col_name in data.columns:            row[col_name] = exp(row[col_name]) # 把指数结果返回给data    return data

读文件

UnicodeDecodeError: 'gbk' codec can't decode byte 0x80 in position 205: illegal multibyte sequence

FILE_OBJECT= open('order.log','r', encoding='UTF-8')

实例

import jsonimport pandas as pdimport pymongofrom conf import *client = pymongo.MongoClient(MONGO_URL)db = client[MONGO_DB]def get_data_frame():    with open('./data/xxx.dat', mode='r', encoding='utf-8') as f:        # key -> columns，value -> list        data_dict = {            'name' : [],            'contact_name' : [],            'price' : [],            'type_id' : [],            'contact_mobile' : []        }        # 每一行为person的信息        for line in f:            # 将str转成dict            data = json.loads(line)            if not data['price'] == 0.0:                data_dict['name'].append(data['name'])                data_dict['contact_name'].append(data['contact_name'])                data_dict['price'].append(data['price'])                data_dict['type_id'].append(data['type_id'])                data_dict['contact_mobile'].append(data['contact_mobile'])        # 创建DataFrame        columns = ['name', 'contact_name', 'contact_mobile', 'price', 'type_id']        index = [i for i in range(len(data_dict['name']))]        data_frame = pd.DataFrame(data_dict, columns=columns, index=index)    return  data_framedef get_type_frame():    # 数据格式 -> type::star    with open('./data/type_id.dat', mode='r', encoding='utf-8') as f:        type_dict = {            'type_name': [],            'star_name': []        }        for line in f:            line = line.split("::")            type_dict['type_name'].append(line[0])            type_dict['star_name'].append(line[1].strip())        columns = ['star_name', 'type_name']        index = [i for i in range(len(type_dict['type_name']))]        type_frame = pd.DataFrame(type_dict, columns=columns, index=index)    return type_framedef main():    data_frame = get_data_frame()    type_frame = get_type_frame()    # 将两个df通过name/star_name进行拼接，得到data_frame.type_id和data_frame.type_name组成columns的type_id_frame    type_id_frame = pd.merge(        data_frame, type_frame, left_on='name', right_on='star_name', sort=False).ix[::, ['type_id', 'type_name']]    # 再通过type_id将type_id_frame插入data_frame    data_frame = pd.merge(data_frame, type_id_frame, on="type_id", sort=False)    # 通过type_id分类，计算得到每个类别的mean和count，插入type_id_frame    type_mean_list = []    type_count_list = []    for type_id in type_id_frame.type_id:        type_mean_list.append(data_frame.ix[data_frame.type_id == type_id, 'price'].mean())        type_count_list.append(data_frame.ix[data_frame.type_id == type_id, 'price'].count())    type_id_frame['type_mean'] = pd.Series(type_mean_list, index=type_id_frame.index)    type_id_frame['type_count'] = pd.Series(type_count_list, index=type_id_frame.index)    # 将type_id_frame根据type_count,type_mean排序之后，将每一行转成Dict，插入mongodb    for index, row in type_id_frame.sort_values(            by=['type_count', 'type_mean'], ascending=False).iterrows():        db[MONGO_TABLE].insert(row.to_dict())    print("ok...")if __name__ == '__main__':    main()