CODEWITHSUNDEEP
pythonpandas
user6446052
user6446052

Reputation: 93

.value_counts() giving truncated results

I have an excel file with a single column of multiple words. I am trying to count the frequency of occurrence of each word. So If I have a list

Labels 
a
a 
b
b
c
c
c

The output should be 

c : 3
b : 2
a : 2

I am using the following the code snippet

import pandas as pd
train = pd.read_csv("ani2.csv")
A = train['Labels'].value_counts()
f = open("ani3.csv",'a')
f.write(str(A))
f.close()

The dataset has about 53000 values and the output I obtained was truncated. The output I obtained was in this format.

z : 1700
y : 1500
x : 1000
...
c : 3
b : 2
a : 2

The values in middle are missing for some reason and all I obtained was three dots.

Upvotes: 5

Views: 4957

Answers (2)

Charlotte Deng
Charlotte Deng

Reputation: 131

You can try this code:

pd.set_option('display.max_rows', 500)
train['Labels'].value_counts()

Upvotes: 3

EdChum
EdChum

Reputation: 394041

You're passing str(A)

just call to_csv on A:

A = train['Labels'].value_counts()
A.to_csv("ani3.csv",mode='a')

When you did str(A) you're converting the output, which will be affected by the pandas display options, to a string representation which is why you get ....

You can see the effect here:

In [34]:
df = pd.DataFrame(np.random.randn(100,1), columns=['a'])
str(df['a'].value_counts())

Out[34]:
'-1.115774    1\n-0.196748    1\n-0.193616    1\n-0.197265    1\n 0.745611    1\n 0.766238    1\n-0.263205    1\n 0.542410    1\n-1.930702    1\n-0.913680    1\n 1.150879    1\n 0.213193    1\n-1.245947    1\n-2.610836    1\n 1.482863    1\n 0.430732    1\n-1.290851    1\n-0.962350    1\n-0.160461    1\n 1.895585    1\n 0.923683    1\n-1.206336    1\n 0.454317    1\n 0.293499    1\n-1.289761    1\n-0.191499    1\n 1.311149    1\n 0.380678    1\n 0.964312    1\n-0.703558    1\n            ..\n-0.384447    1\n 0.172968    1\n-0.221997    1\n 0.133441    1\n-0.343758    1\n-0.897193    1\n-0.525859    1\n-0.226437    1\n-0.552760    1\n-1.991686    1\n 0.517877    1\n 0.659020    1\n 1.680185    1\n 0.155123    1\n-0.788438    1\n-1.364535    1\n 0.034736    1\n 0.494853    1\n 1.113248    1\n-1.449296    1\n 1.123138    1\n-0.747243    1\n-0.429054    1\n-0.567881    1\n-0.476616    1\n-2.630239    1\n 0.084506    1\n 1.250732    1\n 0.071242    1\n-0.432580    1\nName: a, dtype: int64'

Upvotes: 6

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