如何将一行数据从一个熊猫数据存储器复制到另一个熊猫数据存储器?

I have a dataframe of data that I am trying to append to another dataframe. I have tried various ways with .append() and there has been no successful way. When I print the data from iterrows. I provide 2 possible ways I tried to solve the issue below, one creates an error, the other doesn't populate the dataframe with anything.

我有一个要添加到另一个dataframe的数据aframe。我尝试过使用.append()的各种方法，但是没有成功的方法。当我从迭代中打印数据时。我提供了两种可能的方法来解决下面的问题，一种是创建错误，另一种不是用任何东西填充dataframe。

The workflow I am trying to create is create a dataframe based off of a file that contains transaction history of customer orders. I only want to create a single record per order and I am going to add other logic to update the order details based on updates in the history. By the end of the script, it will have a single record for all of the orders and the end state of those orders after iterating through the history file.

我要创建的工作流是基于包含客户订单事务历史的文件创建一个dataframe。我只希望为每个订单创建一条记录，我将添加其他逻辑，根据历史中的更新更新更新更新订单细节。在脚本的末尾，在遍历历史文件之后，它将拥有所有订单的单一记录和这些订单的最终状态。

class om():
"""Manages over the current state of orders"""

def __init__(self,dataF, desc='NONE'):
    self.df = pd.DataFrame
    self.data = dataF
    print type(dataF)
    self.oD= self.df(data=None,columns=desc)

def add_data(self,df):
    for i, row in self.data.iterrows():
        print 'row '+str(row)
        print type(row)
        df.append(self.data[i], ignore_index =True) """ This line creates and error"""
        df.append(row, ignore_index =True) """This line doesn't append anything to the dataframe."""

test = order_manager(body,header)
test.add_data(test.orderData)

1 个解决方案

#1

Use .loc to enlarge the current df. See the example below.

使用。loc放大当前的df。看下面的例子。

import pandas as pd
import numpy as np

date_rng = pd.date_range('2015-01-01', periods=200, freq='D')

df1 = pd.DataFrame(np.random.randn(100, 3), columns='A B C'.split(), index=date_rng[:100])
Out[410]: 
                 A       B       C
2015-01-01  0.2799  0.4416 -0.7474
2015-01-02 -0.4983  0.1490 -0.2599
2015-01-03  0.4101  1.2622 -1.8081
2015-01-04  1.1976 -0.7410  0.4221
2015-01-05  1.3311  1.0399  2.2701
...            ...     ...     ...
2015-04-06 -0.0432  0.6131 -0.0216
2015-04-07  0.4224 -1.1565  2.2285
2015-04-08  0.0663  1.2994  2.0322
2015-04-09  0.1958 -0.4412  0.3924
2015-04-10  0.1622  1.7603  1.4525

[100 rows x 3 columns]


df2 = pd.DataFrame(np.random.randn(100, 3), columns='A B C'.split(), index=date_rng[100:])
Out[411]: 
                 A       B       C
2015-04-11  1.1196 -1.9627  0.6615
2015-04-12 -0.0098  1.7655  0.0447
2015-04-13 -1.7318 -2.0296  0.8384
2015-04-14 -1.5472 -1.7220 -0.3166
2015-04-15  2.5058  0.6487  1.0994
...            ...     ...     ...
2015-07-15 -1.4803  2.1703 -1.9391
2015-07-16 -1.7595 -1.7647 -1.0622
2015-07-17  1.7900  0.2280 -1.8797
2015-07-18  0.7909 -0.4999  0.3848
2015-07-19  1.2243  0.4681 -1.2323

[100 rows x 3 columns]

# to move one row from df2 to df1, use .loc to enlarge df1
# this is far more efficient than pd.concat and pd.append
df1.loc[df2.index[0]] = df2.iloc[0]

Out[413]: 
                 A       B       C
2015-01-01  0.2799  0.4416 -0.7474
2015-01-02 -0.4983  0.1490 -0.2599
2015-01-03  0.4101  1.2622 -1.8081
2015-01-04  1.1976 -0.7410  0.4221
2015-01-05  1.3311  1.0399  2.2701
...            ...     ...     ...
2015-04-07  0.4224 -1.1565  2.2285
2015-04-08  0.0663  1.2994  2.0322
2015-04-09  0.1958 -0.4412  0.3924
2015-04-10  0.1622  1.7603  1.4525
2015-04-11  1.1196 -1.9627  0.6615

[101 rows x 3 columns]