pandas python how to count the number of records or rows in a dataframe
To get the number of rows in a dataframe use:
df.shape[0]
(and df.shape[1]
to get the number of columns).
As an alternative you can use
len(df)
or
len(df.index)
(and len(df.columns)
for the columns)
shape
is more versatile and more convenient than len()
, especially for interactive work (just needs to be added at the end), but len
is a bit faster (see also this answer).
To avoid: count()
because it returns the number of non-NA/null observations over requested axis
len(df.index)
is faster
import pandas as pd
import numpy as np
df = pd.DataFrame(np.arange(24).reshape(8, 3),columns=['A', 'B', 'C'])
df['A'][5]=np.nan
df
# Out:
# A B C
# 0 0 1 2
# 1 3 4 5
# 2 6 7 8
# 3 9 10 11
# 4 12 13 14
# 5 NaN 16 17
# 6 18 19 20
# 7 21 22 23
%timeit df.shape[0]
# 100000 loops, best of 3: 4.22 µs per loop
%timeit len(df)
# 100000 loops, best of 3: 2.26 µs per loop
%timeit len(df.index)
# 1000000 loops, best of 3: 1.46 µs per loop
df.__len__
is just a call to len(df.index)
import inspect
print(inspect.getsource(pd.DataFrame.__len__))
# Out:
# def __len__(self):
# """Returns length of info axis, but here we use the index """
# return len(self.index)
Why you should not use count()
df.count()
# Out:
# A 7
# B 8
# C 8
Regards to your question... counting one Field? I decided to make it a question, but I hope it helps...
Say I have the following DataFrame
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.normal(0, 1, (5, 2)), columns=["A", "B"])
You could count a single column by
df.A.count()
#or
df['A'].count()
both evaluate to 5.
The cool thing (or one of many w.r.t. pandas
) is that if you have NA
values, count takes that into consideration.
So if I did
df['A'][1::2] = np.NAN
df.count()
The result would be
A 3
B 5
Simply, row_num = df.shape[0] # gives number of rows, here's the example:
import pandas as pd
import numpy as np
In [322]: df = pd.DataFrame(np.random.randn(5,2), columns=["col_1", "col_2"])
In [323]: df
Out[323]:
col_1 col_2
0 -0.894268 1.309041
1 -0.120667 -0.241292
2 0.076168 -1.071099
3 1.387217 0.622877
4 -0.488452 0.317882
In [324]: df.shape
Out[324]: (5, 2)
In [325]: df.shape[0] ## Gives no. of rows/records
Out[325]: 5
In [326]: df.shape[1] ## Gives no. of columns
Out[326]: 2