我为Pandas
设置了一个函数,该函数遍历input.csv
中的大量行,并将结果输入到系列中。然后它将系列写入output.csv
。
但是,如果进程中断(例如意外事件),程序将终止,并且所有进入csv的数据都将丢失。
有没有办法将数据连续写入csv,无论该函数是否为所有行完成?
值得一提的是,每次程序启动时,都会创建一个空白output.csv
,并在函数运行时附加到空白处。{/ p>
import pandas as pd
df = pd.read_csv("read.csv")
def crawl(a):
#Create x, y
return pd.Series([x, y])
df[["Column X", "Column Y"]] = df["Column A"].apply(crawl)
df.to_csv("write.csv", index=False)
答案 0 :(得分:13)
这是一种可能的解决方案,它会将数据附加到新文件中,因为它会以块的形式读取csv。如果进程中断,则新文件将包含中断之前的所有信息。
import pandas as pd
#csv file to be read in
in_csv = '/path/to/read/file.csv'
#csv to write data to
out_csv = 'path/to/write/file.csv'
#get the number of lines of the csv file to be read
number_lines = sum(1 for row in (open(in_csv)))
#size of chunks of data to write to the csv
chunksize = 10
#start looping through data writing it to a new file for each chunk
for i in range(1,number_lines,chunksize):
df = pd.read_csv(in_csv,
header=None,
nrows = chunksize,#number of rows to read at each loop
skiprows = i)#skip rows that have been read
df.to_csv(out_csv,
index=False,
header=False,
mode='a',#append data to csv file
chunksize=chunksize)#size of data to append for each loop
答案 1 :(得分:3)
最后,这就是我想出的。谢谢你的帮助!
import pandas as pd
df1 = pd.read_csv("read.csv")
run = 0
def crawl(a):
global run
run = run + 1
#Create x, y
df2 = pd.DataFrame([[x, y]], columns=["X", "Y"])
if run == 1:
df2.to_csv("output.csv")
if run != 1:
df2.to_csv("output.csv", header=None, mode="a")
df1["Column A"].apply(crawl)
答案 2 :(得分:1)
通过使用iterrows()循环数据框并将每行保存到csv文件,我找到了类似问题的解决方案,在您的情况下,它可能是这样的:
for ix, row in df.iterrows():
row['Column A'] = crawl(row['Column A'])
# if you wish to mantain the header
if ix == 0:
df.iloc[ix - 1: ix].to_csv('output.csv', mode='a', index=False, sep=',', encoding='utf-8')
else:
df.iloc[ix - 1: ix].to_csv('output.csv', mode='a', index=False, sep=',', encoding='utf-8', header=False)