[英]Reading in multiple tables from 1 csv file in pandas
suppose I have a csv file like this: 假设我有一个csv文件,如下所示:
Name: Jack
Place: Binghampton
Age:27
Month,Sales,Revenue
Jan,51,$1000
Feb,20,$1050
Mar,100,$10000
### Blank File Space
### Blank File Space
Name: Jill
Place: Hamptonshire
Age: 49
Month,Sales,Revenue
Apr,11,$1000
May,55,$3000
Jun,23,$4600
### Blank File Space
### Blank File Space
...
And the contents of the file are evenly spaced as shown. 文件的内容如图所示均匀分布。 I want to read each Month,Sales,Revenue portion in as its own df.
我想将每个月,销售,收入部分读为自己的df。 I know I can do this manually by doing:
我知道我可以通过以下操作手动完成此操作:
df_Jack = pd.read_csv('./sales.csv', skiprows=3, nrows=3)
df_Jill = pd.read_csv('./sales.csv', skiprows=12, nrows=3)
I'm not even super worried about the names of the df as I think I could do that on my own, I just don't really know how to iterate through the evenly spaced file to find sales records and store them as unique dfs. 我什至不担心df的名称,因为我认为自己可以做到这一点,我只是真的不知道如何遍历间隔均匀的文件来查找销售记录并将其存储为唯一的df。
Thanks for any help in advance! 感谢您的任何帮助!
How about create a list of dfs? 如何创建DFS列表?
from io import StringIO
csvfile = StringIO("""Name: Jack
Place: Binghampton
Age:27
Month,Sales,Revenue
Jan,51,$1000
Feb,20,$1050
Mar,100,$10000
### Blank File Space
### Blank File Space
Name: Jill
Place: Hamptonshire
Age: 49
Month,Sales,Revenue
Apr,11,$1000
May,55,$3000
Jun,23,$4600
### Blank File Space
### Blank File Space""")
df = pd.read_csv(csvfile, sep=',', error_bad_lines=False, names=['Month','Sales','Revenue'])
df1 = df.dropna().loc[df.Month!='Month']
listofdf = [df1[i:i+3] for i in range(0,df1.shape[0],3)]
print(listofdf[0])
Output: 输出:
Month Sales Revenue
4 Jan 51 $1000
5 Feb 20 $1050
6 Mar 100 $10000
print(listofdf[1])
Output: 输出:
Month Sales Revenue
13 Apr 11 $1000
14 May 55 $3000
15 Jun 23 $4600
Obviously you could do this: 显然,您可以这样做:
dfs = [pd.read_csv('./sales.csv', skiprows=i, nrows=3) for i in range(3, n, 9)]
# where n is your expected end line...
But another way is to read the csv yourself and pass the data back to pandas
: 但是另一种方法是自己读取csv并将数据传递回
pandas
:
with open('./sales.csv', 'r') as file:
streaming = True
while streaming:
name = file.readline().rstrip().replace('Name: ','')
for _ in range(2): file.readline()
headers = file.readline().rstrip().split(',')
data = [file.readline().rstrip().split(',') for _ in range(3)]
dfs[name] = pd.DataFrame.from_records(data, columns=headers)
for _ in range(2):
streaming = file.readline()
I'll concede it's quite brutish and inelegant compared to the other answer... but it works. 与其他答案相比,我承认这是蛮残忍的,但确实有效。 And it actually gives you the
DataFrame
by name within a dictionary: 实际上,它通过字典中的名称为您提供了
DataFrame
:
>>> dfs['Jack']
Month Sales Revenue
0 Jan 51 $1000
1 Feb 20 $1050
2 Mar 100 $10000
>>> dfs['Jill']
Month Sales Revenue
0 Apr 11 $1000
1 May 55 $3000
2 Jun 23 $4600
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