熊猫计算行内填充单元格的数量

Question

I have a large dataset with columns labelled from 1 - 65 (among other titled columns), and want to find how many of the columns, per row, have a string (of any value) in them. For example, if all rows 1 - 65 are filled, the count should be 65 in this particular row, if only 10 are filled then the count should be 10.

Is there any easy way to do this? I'm currently using the following code, which is taking very long as there are a large number of rows.

array = pd.read_csv(csvlocation, encoding = "ISO-8859-1")

for i in range (0, lengthofarray)
    for k in range(1,66):
        if array[k][i]!="":
            array["count"][i]=array["count"][i]+1

Answer 1

From my understanding of the post and the subsequent comments, you are interested in knowing the number of strings in each row for columns labels 1 through 65. There are two steps, the first is to subset your data down to columns 1 through 65, and then the following is the count the number of strings in each row. To do this:

import pandas as pd
import numpy as np

# create sample data
df = pd.DataFrame({'col1': list('abdecde'),
                   'col2': np.random.rand(7)})

# change one val of column two to string for illustration purposes    
df.loc[3, 'col2'] = 'b'

# to create the subset of columns, you could use 
# subset = [str(num) for num in list(range(1, 66))]
# and then just use df[subset]

# for each row, count the number of columns that have a string value
# applymap operates elementwise, so we are essentially creating 
# a new representation of your data in place, where a 1 represents a 
# string value was there, and a 0 represent not a string.
# we then sum along the rows to get the final counts
col_str_counts = np.sum(df.applymap(lambda x: 1 if isinstance(x, str) else 0), axis=1)

# we changed the column two value above, so to check that the count is 2 for that row idx:
col_str_counts[3]
>>> 2

# and for the subset, it would simply become:
# col_str_counts = np.sum(df[subset].applymap(lambda x: 1 if isinstance(x, str) else 0), axis=1)

Answer 2

You should be able to adapt your problem to this example

Say we have this dataframe

df = pd.DataFrame([["","foo","bar"],["","","bar"],["","",""],["foo","bar","bar"]])

     0    1    2
0       foo  bar
1            bar
2               
3  foo  bar  bar

Then we create a boolean mask where a cell != "" and sum those values

df['count'] = (df != "").sum(1)
print(df)

     0    1    2  count
0       foo  bar      2
1            bar      1
2                     0
3  foo  bar  bar      3

Answer 3

df = pandas.DataFrame([["","foo","bar"],["","","bar"],["","",""],["foo","bar","bar"]])
total_cells = df.size
df['filled_cell_count'] = (df != "").sum(1)
print(f"{df}")
     0    1    2  filled_cell_count
0       foo  bar                  2
1            bar                  1
2                                 0
3  foo  bar  bar                  3

total_filled_cells = df['filled_cell_count'].sum()/total_cells
print()
print(f"Total Filled Cells in dataframe: {total_filled_cells}")
Total Filled Cells in dataframe: 0.5