CODEWITHSUNDEEP
pythonfilecsvfile-iopandas
Tofeeq Ali
Tofeeq Ali

Reputation: 23

Calculating mean of Pandas dataframe in iterations

I have a huge CSV file like this

Code, Duration

101, 32

205, 111

722, 33

205, 67

722, 33

205, 241

Now I am reading file in chunk as file is pretty big. How to calculate avg duration for each code and save it to CSV file?

Thanks

Upvotes: 0

Views: 267

Answers (3)

Alvaro Fuentes
Alvaro Fuentes

Reputation: 17475

You can group by code and store the count and sum of 'Code','Duration'; something like this:

import pandas as pd

def f(g):
    return pd.DataFrame({'count': [g.shape[0]], 'sum': [g['Duration'].sum()]})

reader = pd.read_csv('data.csv',chunksize=2)
acc = pd.DataFrame({})
for chunk in reader:
    acc = acc.add(chunk.groupby('Code').apply(f).reset_index(level=1,drop=True),fill_value=0)

acc['avg'] = acc['sum']/acc['count']
print acc

acc.to_csv('avg_codes.csv',cols=['avg'],index_label='Code')

Output in terminal:

      count  sum         avg
Code                        
101       1   32   32.000000
205       3  419  139.666667
722       2   66   33.000000

Output in file avg_codes.csv:

Code,avg
101,32.0
205,139.66666666666666
722,33.0

Upvotes: 1

HYRY
HYRY

Reputation: 97331

use groupby.size and groupby.sum for every dataframe, and then reduce them to the result:

import numpy as np
import pandas as pd

c = np.random.randint(100, 10000, 100000)
d = np.random.rand(100000)

df = pd.DataFrame({"c":c, "d":d})
r1 = df.groupby("c").d.mean()

counts = []
sums = []
for i in range(10):
    df2 = df[i*10000:(i+1)*10000]
    g = df2.groupby("c").d
    counts.append(g.size())
    sums.append(g.sum())

from functools import partial
func = partial(pd.Series.add, fill_value=0)
r2 = reduce(func,  sums) / reduce(func, counts).astype(float)

You can also use following code for the final step:

r3 = pd.concat(sums, axis=1).sum(axis=1) / pd.concat(counts, axis=1).sum(axis=1).astype(float)

to check the result:

print np.allclose(r1, r2)
print np.allclose(r1, r3)

Upvotes: 1

Steven Rumbalski
Steven Rumbalski

Reputation: 45552

Not pandas but it works and is memory efficient.

import csv
from collections defaultdict

code_counts = defaultdict(int)
code_durations = defaultdict(int)
with open('yourfile.csv', 'rb') as f:
    reader = csv.reader(f)
    next(reader) # discard header row
    for code, duration in reader:
        code_counts[code] += 1
        code_durations[code] += int(duration)    
code_averages = {code: code_duratons[code] / float(code_counts[code]) for code in code_counts}

Upvotes: 1

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