我通过read_csv
读取了相同的文本文件两次。首次获取与该文件中的“ Col6”与特定字符串(MSG)匹配的键列表。这将为我提供一个仅包含与“ Col6”匹配的条目的数据框。然后第二次我读取相同的文件(同样是read_csv
),如果是key1 == key2
,则还要打印一些基于'Col1'的列。
我基本上有两个问题:
1.我可以将两个搜索(read_csv
)组合在一起吗?
2.即使我将两个read_csv
分开,如何读取多个文件?目前,我只读取一个文件(firstFile.txt
),但是我想用'*.txt'
替换文件名,以便对其中的所有read_csv
文件执行*.txt
操作目录。
数据文件如下所示。我想用Col1=12345
打印所有行,因为Col6
的值为'This is a test'
。
Col1 Col2 Col3 Col4 Col5 Col6 - - - - - - 54321 544 657 888 4476 - 12345 345 456 789 1011 'This is a test' 54321 644 857 788 736 - 54321 744 687 898 7436 - 12345 365 856 789 1020 - 12345 385 956 689 1043 - 12345 385 556 889 1055 - 65432 444 676 876 4554 - - - - - - - 54321 544 657 888 776 - 12345 345 456 789 1011 - 54321 587 677 856 7076 - 12345 345 456 789 1011 - 65432 444 676 876 455 - 12345 345 456 789 1011 - 65432 447 776 576 4055 - - - - - - - 65432 434 376 576 4155 -
我使用的脚本是:
import csv
import pandas as pd
import os
import glob
DL_fields1 = ['Col1', 'Col2']
DL_fields2 = ['Col1', 'Col2','Col3', 'Col4', 'Col5', 'Col6']
MSG = 'This is a test'
iter_csv = pd.read_csv('firstFile.txt', chunksize=1000, usecols=DL_fields1, skiprows=1)
df = pd.concat([chunk[chunk['Special_message'] == MSG] for chunk in iter_csv])
for i, row in df.iterrows():
key1 = df.loc[i, 'Col1']
j=0
for line in pd.read_csv('firstFile.txt', chunksize=1, usecols=DL_fields2, skiprows=1, na_values={'a':'Int64'}):
key2 = line.loc[j,'Col1']
j = j + 1
if (key2 == '-'):
continue
elif (int(key1) == int(key2)):
print (line)
答案 0 :(得分:2)
据我了解,您无需两次读取CSV文件。本质上,您希望MSG
中Col6
出现的所有行。您实际上可以在一行中实现这一目标-
MSG = 'This is a test'
iter_csv = pd.read_csv('firstFile.txt', chunksize=1000, usecols=DL_fields1, skiprows=1)
# this gives you all the rows where MSG occurs in Col6
df = iter_csv.loc[iter_csv['Col6'] == MSG, :]
# this gives you all the rows where 12345 in Col1
df_12345 = df.loc[iter_csv['Col1'] == 12345,]
您可以通过这种方式创建数据的多个子集。
要回答问题的第二部分,您可以像这样遍历所有文本文件-
import glob
txt_files = glob.glob("test/*.txt")
for file in txt_files:
with open(file, 'r') as foo:
some_df = pd.read_csv(file)
编辑:这是您遍历文件并使用Col1=12345
和Col6=MSG
-
import glob
from functools import reduce
results_list = []
MSG = 'This is a test'
txt_files = glob.glob("test/*.txt")
for file in txt_files:
with open(file, 'r') as foo:
some_df = pd.read_csv(file, chunksize=1000, usecols=DL_fields1, skiprows=1)
df = iter_csv.loc[iter_csv['Col6'] == MSG, :]
# results_list is a list of all such dataframes
results_list.append(df.loc[iter_csv['Col1'] == 12345, ])
# All results in one big dataframe
result_df = reduce(lambda x,y: pd.concat([x,y]), results_list)