How to count categorical values including zero occurrence?
1
$begingroup$
I want to count number of code by month.
This is my example dataframe.
id month code
0 sally 0 s_A
1 sally 0 s_B
2 sally 0 s_C
3 sally 0 s_D
4 sally 0 s_E
5 sally 0 s_A
6 sally 0 s_A
7 sally 0 s_B
8 sally 0 s_C
9 sally 0 s_A
I transformed to this Series using count().
df.groupby(['id', 'code', 'month']).m.count()
id code month count
sally s_A 0 12
1 10
2 3
7 15
But, I want to include zero occurrence, like this.
id code month count
sally s_A 0 12
1 10
2 3
3 0
4 0
5 0
6 0
7 15
8 0
9 0
10 0
11 0
python pandas
asked Apr 6 '17 at 8:23
planariaplanaria
3814
$endgroup$
add a comment |
1
$begingroup$
I want to count number of code by month.
This is my example dataframe.
id month code
0 sally 0 s_A
1 sally 0 s_B
2 sally 0 s_C
3 sally 0 s_D
4 sally 0 s_E
5 sally 0 s_A
6 sally 0 s_A
7 sally 0 s_B
8 sally 0 s_C
9 sally 0 s_A
I transformed to this Series using count().
df.groupby(['id', 'code', 'month']).m.count()
id code month count
sally s_A 0 12
1 10
2 3
7 15
But, I want to include zero occurrence, like this.
id code month count
sally s_A 0 12
1 10
2 3
3 0
4 0
5 0
6 0
7 15
8 0
9 0
10 0
11 0
python pandas
asked Apr 6 '17 at 8:23
planariaplanaria
3814
$endgroup$
$begingroup$
Without transforming it into a Series, just try this:df['month'].value_counts(), where df is your pandas dataframe
$endgroup$
– Nain
Apr 6 '17 at 9:41
$begingroup$
@Nain thanks, but I need to group by 'sally' and there are missing months like the example above.
$endgroup$
– planaria
Apr 6 '17 at 9:44
$begingroup$
@Kyle. Could you explain more? Months are not complete. How can I insert month with zero count?
$endgroup$
– planaria
Apr 6 '17 at 22:21
$begingroup$
df.month.value_counts(dropna=False)
$endgroup$
– Andrew L
May 15 '17 at 9:03
add a comment |
1
1
1
$begingroup$
I want to count number of code by month.
This is my example dataframe.
id month code
0 sally 0 s_A
1 sally 0 s_B
2 sally 0 s_C
3 sally 0 s_D
4 sally 0 s_E
5 sally 0 s_A
6 sally 0 s_A
7 sally 0 s_B
8 sally 0 s_C
9 sally 0 s_A
I transformed to this Series using count().
df.groupby(['id', 'code', 'month']).m.count()
id code month count
sally s_A 0 12
1 10
2 3
7 15
But, I want to include zero occurrence, like this.
id code month count
sally s_A 0 12
1 10
2 3
3 0
4 0
5 0
6 0
7 15
8 0
9 0
10 0
11 0
python pandas
asked Apr 6 '17 at 8:23
planariaplanaria
3814
$endgroup$
I want to count number of code by month.
This is my example dataframe.
id month code
0 sally 0 s_A
1 sally 0 s_B
2 sally 0 s_C
3 sally 0 s_D
4 sally 0 s_E
5 sally 0 s_A
6 sally 0 s_A
7 sally 0 s_B
8 sally 0 s_C
9 sally 0 s_A
I transformed to this Series using count().
df.groupby(['id', 'code', 'month']).m.count()
id code month count
sally s_A 0 12
1 10
2 3
7 15
But, I want to include zero occurrence, like this.
id code month count
sally s_A 0 12
1 10
2 3
3 0
4 0
5 0
6 0
7 15
8 0
9 0
10 0
11 0
python pandas
python pandas
asked Apr 6 '17 at 8:23
planariaplanaria
3814
asked Apr 6 '17 at 8:23
planariaplanaria
3814
asked Apr 6 '17 at 8:23
planariaplanaria
3814
asked Apr 6 '17 at 8:23
planariaplanaria
3814
asked Apr 6 '17 at 8:23
planariaplanaria
3814
3814
$begingroup$
Without transforming it into a Series, just try this:df['month'].value_counts(), where df is your pandas dataframe
$endgroup$
– Nain
Apr 6 '17 at 9:41
$begingroup$
@Nain thanks, but I need to group by 'sally' and there are missing months like the example above.
$endgroup$
– planaria
Apr 6 '17 at 9:44
$begingroup$
@Kyle. Could you explain more? Months are not complete. How can I insert month with zero count?
$endgroup$
– planaria
Apr 6 '17 at 22:21
$begingroup$
df.month.value_counts(dropna=False)
$endgroup$
– Andrew L
May 15 '17 at 9:03
add a comment |
$begingroup$
Without transforming it into a Series, just try this:df['month'].value_counts(), where df is your pandas dataframe
$endgroup$
– Nain
Apr 6 '17 at 9:41
$begingroup$
@Nain thanks, but I need to group by 'sally' and there are missing months like the example above.
$endgroup$
– planaria
Apr 6 '17 at 9:44
$begingroup$
@Kyle. Could you explain more? Months are not complete. How can I insert month with zero count?
$endgroup$
– planaria
Apr 6 '17 at 22:21
$begingroup$
df.month.value_counts(dropna=False)
$endgroup$
– Andrew L
May 15 '17 at 9:03
$begingroup$
Without transforming it into a Series, just try this:
df['month'].value_counts(), where df is your pandas dataframe$endgroup$
– Nain
Apr 6 '17 at 9:41
$begingroup$
Without transforming it into a Series, just try this:
df['month'].value_counts(), where df is your pandas dataframe$endgroup$
– Nain
Apr 6 '17 at 9:41
$begingroup$
@Nain thanks, but I need to group by 'sally' and there are missing months like the example above.
$endgroup$
– planaria
Apr 6 '17 at 9:44
$begingroup$
@Nain thanks, but I need to group by 'sally' and there are missing months like the example above.
$endgroup$
– planaria
Apr 6 '17 at 9:44
$begingroup$
@Kyle. Could you explain more? Months are not complete. How can I insert month with zero count?
$endgroup$
– planaria
Apr 6 '17 at 22:21
$begingroup$
@Kyle. Could you explain more? Months are not complete. How can I insert month with zero count?
$endgroup$
– planaria
Apr 6 '17 at 22:21
$begingroup$
df.month.value_counts(dropna=False)$endgroup$
– Andrew L
May 15 '17 at 9:03
$begingroup$
df.month.value_counts(dropna=False)$endgroup$
– Andrew L
May 15 '17 at 9:03
add a comment |
1 Answer
1
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oldest
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$begingroup$
Based on the short example DataFrame you provided, this block of code will include all of the months. It is based on using the Series.reindex method and creating a new MultiIndex with the additional values for the months:
import pandas as pd
# Load example data into DataFrame
df = pd.read_table("categorical_data.txt", delim_whitespace=True)
# Transform to a count
count = df.groupby(['id', 'code', 'month']).month.count()
# Re-create a new array of levels, now including all 12 months
levels = [count.index.levels[0].values, count.index.levels[1].values, range(12)]
new_index = pd.MultiIndex.from_product(levels, names=count.index.names)
# Reindex the count and fill empty values with zero (NaN by default)
count = count.reindex(new_index, fill_value=0)
print(count)
Printing the result, I get something like this (only showing the first entry for sally/s_A):
id code month
sally s_A 0 4
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 0
edited 15 mins ago
Tomasz Gandor
1033
answered Nov 8 '17 at 22:43
ffuffu
364
$endgroup$
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
2
$begingroup$
Based on the short example DataFrame you provided, this block of code will include all of the months. It is based on using the Series.reindex method and creating a new MultiIndex with the additional values for the months:
import pandas as pd
# Load example data into DataFrame
df = pd.read_table("categorical_data.txt", delim_whitespace=True)
# Transform to a count
count = df.groupby(['id', 'code', 'month']).month.count()
# Re-create a new array of levels, now including all 12 months
levels = [count.index.levels[0].values, count.index.levels[1].values, range(12)]
new_index = pd.MultiIndex.from_product(levels, names=count.index.names)
# Reindex the count and fill empty values with zero (NaN by default)
count = count.reindex(new_index, fill_value=0)
print(count)
Printing the result, I get something like this (only showing the first entry for sally/s_A):
id code month
sally s_A 0 4
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 0
edited 15 mins ago
Tomasz Gandor
1033
answered Nov 8 '17 at 22:43
ffuffu
364
$endgroup$
add a comment |
2
$begingroup$
Based on the short example DataFrame you provided, this block of code will include all of the months. It is based on using the Series.reindex method and creating a new MultiIndex with the additional values for the months:
import pandas as pd
# Load example data into DataFrame
df = pd.read_table("categorical_data.txt", delim_whitespace=True)
# Transform to a count
count = df.groupby(['id', 'code', 'month']).month.count()
# Re-create a new array of levels, now including all 12 months
levels = [count.index.levels[0].values, count.index.levels[1].values, range(12)]
new_index = pd.MultiIndex.from_product(levels, names=count.index.names)
# Reindex the count and fill empty values with zero (NaN by default)
count = count.reindex(new_index, fill_value=0)
print(count)
Printing the result, I get something like this (only showing the first entry for sally/s_A):
id code month
sally s_A 0 4
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 0
edited 15 mins ago
Tomasz Gandor
1033
answered Nov 8 '17 at 22:43
ffuffu
364
$endgroup$
add a comment |
2
2
2
$begingroup$
Based on the short example DataFrame you provided, this block of code will include all of the months. It is based on using the Series.reindex method and creating a new MultiIndex with the additional values for the months:
import pandas as pd
# Load example data into DataFrame
df = pd.read_table("categorical_data.txt", delim_whitespace=True)
# Transform to a count
count = df.groupby(['id', 'code', 'month']).month.count()
# Re-create a new array of levels, now including all 12 months
levels = [count.index.levels[0].values, count.index.levels[1].values, range(12)]
new_index = pd.MultiIndex.from_product(levels, names=count.index.names)
# Reindex the count and fill empty values with zero (NaN by default)
count = count.reindex(new_index, fill_value=0)
print(count)
Printing the result, I get something like this (only showing the first entry for sally/s_A):
id code month
sally s_A 0 4
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 0
edited 15 mins ago
Tomasz Gandor
1033
answered Nov 8 '17 at 22:43
ffuffu
364
$endgroup$
Based on the short example DataFrame you provided, this block of code will include all of the months. It is based on using the Series.reindex method and creating a new MultiIndex with the additional values for the months:
import pandas as pd
# Load example data into DataFrame
df = pd.read_table("categorical_data.txt", delim_whitespace=True)
# Transform to a count
count = df.groupby(['id', 'code', 'month']).month.count()
# Re-create a new array of levels, now including all 12 months
levels = [count.index.levels[0].values, count.index.levels[1].values, range(12)]
new_index = pd.MultiIndex.from_product(levels, names=count.index.names)
# Reindex the count and fill empty values with zero (NaN by default)
count = count.reindex(new_index, fill_value=0)
print(count)
Printing the result, I get something like this (only showing the first entry for sally/s_A):
id code month
sally s_A 0 4
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 0
edited 15 mins ago
Tomasz Gandor
1033
answered Nov 8 '17 at 22:43
ffuffu
364
edited 15 mins ago
Tomasz Gandor
1033
edited 15 mins ago
Tomasz Gandor
1033
edited 15 mins ago
Tomasz Gandor
1033
1033
answered Nov 8 '17 at 22:43
ffuffu
364
answered Nov 8 '17 at 22:43
ffuffu
364
answered Nov 8 '17 at 22:43
ffuffu
364
364
add a comment |
add a comment |
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$begingroup$
Without transforming it into a Series, just try this:
df['month'].value_counts(), where df is your pandas dataframe$endgroup$
– Nain
Apr 6 '17 at 9:41
$begingroup$
@Nain thanks, but I need to group by 'sally' and there are missing months like the example above.
$endgroup$
– planaria
Apr 6 '17 at 9:44
$begingroup$
@Kyle. Could you explain more? Months are not complete. How can I insert month with zero count?
$endgroup$
– planaria
Apr 6 '17 at 22:21
$begingroup$
df.month.value_counts(dropna=False)$endgroup$
– Andrew L
May 15 '17 at 9:03