Count categorical values in DataFrame
I have DataFrame only with Categorical Values
Col1 | Col2| ... | ColM
Row
1 X | Y | ... | X
2 Z | X | ... | Y
3 Y | Z | ... | X
.
.
.
N X | Z | ... | Z
I would like to count how many times each category appeared in database
So example result:
X - 100 times
Y - 30 times
Z = 210 times
Thank You for help
python-3.x pandas dataframe
asked Dec 27 '18 at 18:22
goskan
528
add a comment |
I have DataFrame only with Categorical Values
Col1 | Col2| ... | ColM
Row
1 X | Y | ... | X
2 Z | X | ... | Y
3 Y | Z | ... | X
.
.
.
N X | Z | ... | Z
I would like to count how many times each category appeared in database
So example result:
X - 100 times
Y - 30 times
Z = 210 times
Thank You for help
python-3.x pandas dataframe
asked Dec 27 '18 at 18:22
goskan
528
3
df.stack().value_counts()?
– coldspeed
Dec 27 '18 at 18:23
add a comment |
I have DataFrame only with Categorical Values
Col1 | Col2| ... | ColM
Row
1 X | Y | ... | X
2 Z | X | ... | Y
3 Y | Z | ... | X
.
.
.
N X | Z | ... | Z
I would like to count how many times each category appeared in database
So example result:
X - 100 times
Y - 30 times
Z = 210 times
Thank You for help
python-3.x pandas dataframe
asked Dec 27 '18 at 18:22
goskan
528
I have DataFrame only with Categorical Values
Col1 | Col2| ... | ColM
Row
1 X | Y | ... | X
2 Z | X | ... | Y
3 Y | Z | ... | X
.
.
.
N X | Z | ... | Z
I would like to count how many times each category appeared in database
So example result:
X - 100 times
Y - 30 times
Z = 210 times
Thank You for help
python-3.x pandas dataframe
python-3.x pandas dataframe
asked Dec 27 '18 at 18:22
goskan
528
asked Dec 27 '18 at 18:22
goskan
528
asked Dec 27 '18 at 18:22
goskan
528
asked Dec 27 '18 at 18:22
goskan
528
asked Dec 27 '18 at 18:22
goskan
528
528
3
df.stack().value_counts()?
– coldspeed
Dec 27 '18 at 18:23
add a comment |
3
df.stack().value_counts()?
– coldspeed
Dec 27 '18 at 18:23
3
3
df.stack().value_counts()?– coldspeed
Dec 27 '18 at 18:23
df.stack().value_counts()?– coldspeed
Dec 27 '18 at 18:23
add a comment |
1 Answer
1
active
oldest
votes
The most performant option is to use np.unique with the return_counts flag set:
u, c = np.unique(df, return_counts=True)
pd.Series(c, index=u)
There's also stack and value_counts, which is much slower, but simple and intuitive:
df.stack().value_counts()
answered Dec 27 '18 at 18:25
coldspeed
120k20119195
1
That's the answer, I was thinking aboutvalue_counts()but completely didn't think aboutstack(). Thank You!
– goskan
Dec 27 '18 at 18:28
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
The most performant option is to use np.unique with the return_counts flag set:
u, c = np.unique(df, return_counts=True)
pd.Series(c, index=u)
There's also stack and value_counts, which is much slower, but simple and intuitive:
df.stack().value_counts()
answered Dec 27 '18 at 18:25
coldspeed
120k20119195
1
That's the answer, I was thinking aboutvalue_counts()but completely didn't think aboutstack(). Thank You!
– goskan
Dec 27 '18 at 18:28
add a comment |
The most performant option is to use np.unique with the return_counts flag set:
u, c = np.unique(df, return_counts=True)
pd.Series(c, index=u)
There's also stack and value_counts, which is much slower, but simple and intuitive:
df.stack().value_counts()
answered Dec 27 '18 at 18:25
coldspeed
120k20119195
1
That's the answer, I was thinking aboutvalue_counts()but completely didn't think aboutstack(). Thank You!
– goskan
Dec 27 '18 at 18:28
add a comment |
The most performant option is to use np.unique with the return_counts flag set:
u, c = np.unique(df, return_counts=True)
pd.Series(c, index=u)
There's also stack and value_counts, which is much slower, but simple and intuitive:
df.stack().value_counts()
answered Dec 27 '18 at 18:25
coldspeed
120k20119195
The most performant option is to use np.unique with the return_counts flag set:
u, c = np.unique(df, return_counts=True)
pd.Series(c, index=u)
There's also stack and value_counts, which is much slower, but simple and intuitive:
df.stack().value_counts()
answered Dec 27 '18 at 18:25
coldspeed
120k20119195
answered Dec 27 '18 at 18:25
coldspeed
120k20119195
answered Dec 27 '18 at 18:25
coldspeed
120k20119195
answered Dec 27 '18 at 18:25
coldspeed
120k20119195
120k20119195
1
That's the answer, I was thinking aboutvalue_counts()but completely didn't think aboutstack(). Thank You!
– goskan
Dec 27 '18 at 18:28
add a comment |
1
That's the answer, I was thinking aboutvalue_counts()but completely didn't think aboutstack(). Thank You!
– goskan
Dec 27 '18 at 18:28
1
1
That's the answer, I was thinking about
value_counts() but completely didn't think about stack(). Thank You!– goskan
Dec 27 '18 at 18:28
That's the answer, I was thinking about
value_counts() but completely didn't think about stack(). Thank You!– goskan
Dec 27 '18 at 18:28
add a comment |
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3
df.stack().value_counts()?– coldspeed
Dec 27 '18 at 18:23