Python 如何将一行从一个熊猫数据帧复制到另一个熊猫数据帧？
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How do I copy a row from one pandas dataframe to another pandas dataframe?
提问by Chris
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.
我有一个数据数据框，我试图将其附加到另一个数据框。我用 .append() 尝试了各种方法，但没有成功的方法。当我从 iterrows 打印数据时。我提供了两种可能的方法来解决下面的问题，一种会产生错误，另一种不会用任何东西填充数据框。
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.
我尝试创建的工作流程是基于包含客户订单交易历史记录的文件创建一个数据框。我只想为每个订单创建一条记录，我将添加其他逻辑以根据历史记录中的更新来更新订单详细信息。在脚本结束时，在遍历历史文件后，它将为所有订单和这些订单的结束状态创建一条记录。
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)
采纳答案by Jianxun Li
Use .locto enlarge the current df. See the example below.
使用.loc放大电流df。请参阅下面的示例。
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]
