使用 pandas 对列表进行排序和安排 [英] Sorting and arranging a list using pandas
问题描述
我有一个如下所示的输入文件，该文件需要按以下顺序排列:键值需要按升序排列，而最后一个不存在的键则需要打印出来. 我正在按要求的格式排列数据，但顺序丢失了.
I have an input file as shown below which needs to be arranged in such an order that the key values need to be in ascending order, while the keys which are not present need to be printed in the last. I am getting the data arranged in the required format but the order is missing.
我尝试使用sort()方法，但显示列表没有属性排序". 请提出解决方案，并提出是否需要任何修改.
I have tried using sort() method but it shows "list has no attribute sort". Please suggest solution and also suggest if any modifications required.
输入文件:
3=1388|4=1388|5=IBM|8=157.75|9=88929|1021=1500|854=n|388=157.75|394=157.75|474=157.75|1584=88929|444=20160713|459=93000546718000|461=7|55=93000552181000|22=89020|400=157.75|361=0.73|981=0|16=1468416600.6006|18=1468416600.6006|362=0.46
3=1388|4=1388|5=IBM|8=157.73|9=100|1021=0|854=p|394=157.73|474=157.749977558|1584=89029|444=20160713|459=93001362639104|461=26142|55=93001362849000|22=89120|361=0.71|981=0|16=1468416601.372|18=1468416601.372|362=0.45
3=1388|4=1388|5=IBM|8=157.69|9=100|1021=600|854=p|394=157.69|474=157.749910415|1584=89129|444=20160713|459=93004178882560|461=27052|55=93004179085000|22=89328|361=0.67|981=1|16=1468416604.1916|18=1468416604.1916|362=0.43
我尝试的代码:
import pandas as pd
import numpy as np
df = pd.read_csv('inputfile', index_col=None, names=['text'])
s = df.text.str.split('|')
ds = [dict(w.split('=', 1) for w in x) for x in s]
p = pd.DataFrame.from_records(ds)
p1 = p.replace(np.nan,'n/a', regex=True)
st = p1.stack(level=0,dropna=False)
dfs = [g for i,g in st.groupby(level=0)]
#print st
i = 0
while i < len(dfs):
#index of each column
print ('\nindex[%d]'%i)
for (_,k),v in dfs[i].iteritems():
print k,'\t',v
i = i + 1
输出获取:
index[0]
1021 1500
1584 88929
16 1468416600.6006
18 1468416600.6006
22 89020
3 1388
361 0.73
362 0.46
388 157.75
394 157.75
4 1388
400 157.75
444 20160713
459 93000546718000
461 7
474 157.75
5 IBM
55 93000552181000
8 157.75
854 n
9 88929
981 0
index[1]
1021 0
1584 89029
16 1468416601.372
18 1468416601.372
22 89120
3 1388
361 0.71
362 0.45
388 n/a
394 157.73
4 1388
400 n/a
444 20160713
459 93001362639104
461 26142
474 157.749977558
5 IBM
55 93001362849000
8 157.73
854 p
9 100
981 0
预期输出:
index[0]
3 1388
4 1388
5 IBM
8 157.75
9 88929
16 1468416600.6006
18 1468416600.6006
22 89020
55 93000552181000
361 0.73
362 0.46
388 157.75
394 157.75
400 157.75
444 20160713
459 93000546718000
461 7
474 157.75
854 n
981 0
1021 1500
1584 88929
index[1]
3 1388
4 1388
5 IBM
8 157.75
9 88929
16 1468416600.6006
18 1468416600.6006
22 89020
55 93000552181000
361 0.73
362 0.46
394 157.75
444 20160713
459 93000546718000
461 7
474 157.75
854 n
981 0
1021 1500
1584 88929
388 n/a
400 n/a
推荐答案
用
ds = [{int(pair[0]): pair[1] for pair in [w.split('=', 1) for w in x]} for x in s]
将索引转换为整数，以便对其进行数字排序
To convert the index to an integer so it will be sorted numerically
要在最后输出n/a值，可以使用pandas选择首先输出非空值，然后输出空值，例如:
To output the n/a values at the end, you could use the pandas selection to output the nonnull values first, then the null values, e.g:
for (ix, series) in p.iterrows():
print('\nindex[%d]' % ix)
output_series(ix, series[pd.notnull])
output_series(ix, series[pd.isnull].fillna('n/a'))
顺便说一句，您还可以简化堆栈，分组方式，打印到:
btw, you can also simplify your stack, groupby, print to:
for (ix, series) in p1.iterrows():
print('\nindex[%d]' % ix)
for tag, value in series.iteritems():
print(tag, '\t', value)
因此整个脚本变为:
def output_series(ix, series):
for tag, value in series.iteritems():
print(tag, '\t', value)
df = pd.read_csv('inputfile', index_col=None, names=['text'])
s = df.text.str.split('|')
ds = [{int(pair[0]): pair[1] for pair in [w.split('=', 1) for w in x]} for x in s]
p = pd.DataFrame.from_records(ds)
for (ix, series) in p.iterrows():
print('\nindex[%d]' % ix)
output_series(ix, series[pd.notnull])
output_series(ix, series[pd.isnull].fillna('n/a'))
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