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count 0's in a zoo (or dataframe) object


count 0's in a zoo (or dataframe) object

By : maiterela
Date : October 19 2020, 08:10 PM
fixed the issue. Will look into that further Create a test zoo object z and then use the indicated expression. If you know there are no NAs then the na.rm = TRUE argument can be omitted. This also works for a data frame and also for a matrix.
code :
library(zoo)

# test data
z <- zoo(BOD)
z[2:3, 1] <- 0
z[4, 2] <- 0
z[5, 1] <- NA

colSums(z == 0, na.rm = TRUE)
  Time demand 
     2      1 


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How to count number of categories in DataFrame object?

How to count number of categories in DataFrame object?


By : Thiago Souza
Date : March 29 2020, 07:55 AM
hop of those help? Let say we have a DataFrame object with number of boxes. Each box has a fruit inside 'Apple', 'Banana' or 'Peach'. , Do you mean something such as:
code :
from collections import Counter
df = pd.DataFrame({'a':['apple','apple','banana','peach', 'banana', 'apple']})

print Counter(df['a'])
>> Counter({'apple': 3, 'banana': 2, 'peach': 1})
df = pd.DataFrame({'a':['apple','apple','banana','peach', 'banana', 'apple']})

print df.groupby(['a']).size()
>> a
   apple     3
   banana    2
   peach     1
How do I convert a flat DataFrame object to a DataFrame object with multiple column levels that is also grouped by a spe

How do I convert a flat DataFrame object to a DataFrame object with multiple column levels that is also grouped by a spe


By : Marc.255
Date : March 29 2020, 07:55 AM
help you fix your problem I'm assuming you want to use all ['W', 'X', 'Y', 'Z'] columns, and only one of the date columns. If so, the below should get you there - if you first apply set_index and then unstack, pandas creates the MultiIndex automatically, which you can then swap by level and sort as you wish:
code :
df.columns.name = 'metrics'    
df = df.set_index(['tdate', 'tdim']).drop('stringdate', axis=1).unstack('tdate')
df = df.swaplevel(0,1,axis=1).sort_index(axis=1)

     tdate    11/19/15                                11/20/15                      \
metrics         W         X         Y         Z         W         X         Y   
tdim                                                                            
a             NaN       NaN       NaN       NaN       NaN       NaN       NaN   
b       -0.192996  0.150177 -0.684978  0.464223       NaN       NaN       NaN   
c             NaN       NaN       NaN       NaN  0.335737 -0.380237 -0.473996   

tdate             12/17/15             ...       5/25/15            5/31/15  \
metrics        Z         W        X    ...             Y        Z         W   
tdim                                   ...                                    
a            NaN       NaN      NaN    ...           NaN      NaN -0.572807   
b            NaN  1.425946  1.29343    ...           NaN      NaN       NaN   
c        -1/1/04       NaN      NaN    ...     -0.834495  -1/1/04       NaN   

tdate                                    7/25/15                                
metrics         X         Y          Z         W         X        Y          Z  
tdim                                                                            
a       -2.484145 -2.339342  -0.263439       NaN       NaN      NaN        NaN  
b             NaN       NaN        NaN       NaN       NaN      NaN        NaN  
c             NaN       NaN        NaN -1.089473 -1.375843 -0.90125  -0.775122 
Whats the fastest way to loop through a DataFrame and count occurrences within the DataFrame whilst some condition is fu

Whats the fastest way to loop through a DataFrame and count occurrences within the DataFrame whilst some condition is fu


By : Amap
Date : March 29 2020, 07:55 AM
like below fixes the issue I have a dataframe with two Boolean fields (as below). , How about this:
code :
df['a3'] = df.apply(lambda x: 0 if not x.a1 else len(df.a2[x.name:df.a2.tolist()[x.name:].index(True)+x.name]), axis=1)
Python pandas dataframe: How to count and show the number of missing value in dataframe only?

Python pandas dataframe: How to count and show the number of missing value in dataframe only?


By : madusmacus
Date : March 29 2020, 07:55 AM
this one helps. In my opinion simpliest is remove 0 values by boolean indexing and then sort_values:
code :
s = df.isna().sum()
s = s[s != 0].sort_values(ascending=False)
df1 = df.isna()
s = df1.loc[:, df1.any()].sum().sort_values(ascending=False)
df = pd.DataFrame({
        'A':list('abcdef'),
         'B':[np.nan,5,np.nan,5,5,np.nan],
         'C':[7,8,9,np.nan,2,3],
         'D':[1,3,5,7,1,0],
         'E':[np.nan,3,6,9,2,np.nan],
         'F':list('aaabbb')
})

s = df.isna().sum()
s = s[s != 0].sort_values(ascending=False)
print (s)
B    3
E    2
C    1
dtype: int64
Is there a way in Pandas to count (Countifs in excel) in one dataframe and add counts as new column in another dataframe

Is there a way in Pandas to count (Countifs in excel) in one dataframe and add counts as new column in another dataframe


By : user2242884
Date : March 29 2020, 07:55 AM
Hope this helps Use map by Series:
code :
df2['count'] = df2['group_id'].map(df1.groupby('id')['id'].count())
df2['count'] = df2['group_id'].map(df1['id'].value_counts())
print (df2)
   group_id group_name  count
0     48299      e_sys      4
1     50774        Y3N      1
2     64865       nana      2
3     48865      juzti      3
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