python中使用熊貓的頻率表

Question

我使用pandas lineSer.value_counts（）創建頻率表，但它不顯示我所有的項目。我有超過100個數據，我需要看到他們的

def freqTable(): 
    fileIn = open('data.txt','r') 
    fileOut = open('dataOut.txt', 'w') 
    lines = [line.strip() for line in fileIn if line.strip() and not line.startswith('com') 
    lineSer = pd.Series(lines) 
    freq = str(lineSer.value_counts()) 
    for line in freq: 
     fileOut.write(line) 

這是我使用的代碼，我需要的結果，以擺脫「...」，看到所有的數據點。我可以做什麼不同？

Madding.  57 
Crowning. 47 
My.   8 
And.   8 
Thy.   7 
Thou.   7 
The.   5 
To.   5 
For.   5 
I.   4 
That.   4 
In.   4 
Love.   4 
Is.   3 
Not.   3 
... 
Did.   1 
Shadows.  1 
Of.   1 
Mind,.  1 
O'erlook.  1 
Sometime.  1 
Fairer.  1 
Monsters,. 1 
23.   1 
Defect,.  1 
Show,.  1 
What's.  1 
Wood.   1 
So.   1 
Lov'st,.  1 
Length: 133, dtype: int64 

Answer 1

試試這個：

pd.options.display.max_rows = 999 

Answer 2

如果你想寫列表中的文件時，不要使之成爲一個字符串，並寫一個文件。 Pandas具有內置函數來將文件寫入文件。只要做lineSer.value_counts().to_csv('dataOut.txt')。如果您想調整輸出的格式，請閱讀to_csv的文檔以瞭解如何對其進行自定義。 （您可以也可能使用類似pandas.read_csv讀取更有效的數據，但這是另一個話題。）

Answer 3

如果你需要臨時的數據顯示，嘗試option_context與display.max_rows：

#temporary print 999 rows 
with pd.option_context('display.max_rows', 999): 
    print freq 

更多信息在docs 。

我嘗試使用功能strip和startswith修改您的解決方案，用於字符串數據的工作和to_csv寫入輸出file：

import pandas as pd 
import io 

temp=u"""Madding. 
Madding. 
    Madding. 
Madding. 
Crowning. 
    Crowning. 
com Crowning. 
com My. 
    com And. 
    Thy. 
Thou. 
The.""" 
#after testing replace io.StringIO(temp) to data.txt 
s = pd.read_csv(io.StringIO(temp), sep="|", squeeze=True) 
print s 
0   Madding. 
1   Madding. 
2   Madding. 
3   Crowning. 
4   Crowning. 
5  com Crowning. 
6   com My. 
7   com And. 
8    Thy. 
9    Thou. 
10    The. 
Name: Madding., dtype: object 

#strip data 
s = s.str.strip() 

#get data which starts with 'com' 
print s.str.startswith('com') 
0  False 
1  False 
2  False 
3  False 
4  False 
5  True 
6  True 
7  True 
8  False 
9  False 
10 False 
Name: Madding., dtype: bool 

#filter rows, which not starts width 'com' 
s = s[~s.str.startswith('com')] 
print s 
0  Madding. 
1  Madding. 
2  Madding. 
3  Crowning. 
4  Crowning. 
8   Thy. 
9   Thou. 
10   The. 
Name: Madding., dtype: object 

#count freq 
freq = s.value_counts() 

#temporary print 999 rows 
with pd.option_context('display.max_rows', 999): 
    print freq 
Madding.  3 
Crowning. 2 
Thou.  1 
Thy.   1 
The.   1 
Name: Madding., dtype: int64 

#write series to file by to_csv 
freq.to_csv('dataOut.txt', sep=';')