iterate over a list of colums to print out the .value_counts()

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I have a list of columns i want to iterate over to get the .value_counts() for each column , getting errors or the code i posted in the bottom i get no printing at all

x = [ 'call_type','date_time','FullAddress','priority']

for i in range(len(x)):
    df[x[i]].value_counts()

this is with one single column name

df["call_type"].value_counts()

415         22303
459A        21045
1150        17070
1151        12884
911         11094
CW           9458
586          9405
5150         7109
415V         6922
1016         6453
MCTSTP       5818
1185         5682
FU           5179
1186         5101
415N         5066
SELENF       4787
FD           4435
SLEEPER      3885
INFO         3511
REPORT       3390
1153         3264
PARTY        3170
10851R       2923
602          2877
242          2831
459R         2825
AU2          2802
CC           2776
415PP        2528
488R         2525

You are just generating data, but not telling your function to print the data to the console.

Add the print() function

x = ['call_type','date_time','FullAddress','priority']

for i in range(len(x)):
    print(df[x[i]].value_counts())

print value_counts for columns in a loop, You have an extra pair of square brackets in your chart_cols. Replace the first line by chart_cols = 'respondent_age respondent_gender� Iterate Over columns in dataframe by index using iloc[] To iterate over the columns of a Dataframe by index we can iterate over a range i.e. 0 to Max number of columns then for each index we can select the columns contents using iloc[]. Let’s see how to iterate over all columns of dataframe from 0th index to last index i.e.

Your solution should working, also is possible simplify:

for i in x:
    print(df[i].value_counts())

Getting frequency counts of a columns in Pandas DataFrame , Method #1: Using Series.value_counts() print (count) After grouping a DataFrame object on one column, we can apply count() method on� Pandas : Loop or Iterate over all or certain columns of a dataframe 2 Comments Already Todd - January 27th, 2020 at 9:08 am none Comment author #28764 on Python : How to Iterate over a list ? by thispointer.com

Also,

for col in df.columns:
    print(df[col].value_counts())

or,

df.apply(lambda x: x.value_counts()).T.stack()

Pandas, How to list available columns on a DataFrame; How to iterate over a DataFrame for item, row in df.iterrows(): print row() df[column].value_counts() # get indexes df[column].value_counts().index.tolist() # get values of occurrences to enter missing months on your dataframe with empty values on them. Iteration is a general term for taking each item of something, one after another. Pandas DataFrame consists of rows and columns so, in order to iterate over dataframe, we have to iterate a dataframe like a dictionary.

Pandas iterate over columns? : learnpython, If I want to perform an operation on each column of a pandas dataframe, is it How would I go about using apply() with value_counts()? I tried it, but get an Instead of just saying print('your number is too high') I changed it to print('you're too big, {"class": "item-list-class"}) # empty dictionary to store data, could be a list of� To iterate through columns we need to do just a bit more manual work, creating a list of dataframe columns and then iterating through that list to pull out the dataframe columns: columns = list(df) for column in columns: print (df[column][2]) # print the third element of the column OUT: Frodo Hobbit 4 2 5

Pandas : Get frequency of a value in dataframe column/index & find , List of Tuples On calling value_counts() on this Series object, it returns an another Series print("Frequency of value in column 'Age' including NaN :") Pandas : Loop or Iterate over all or certain columns of a dataframe� Loop through Entire Column Below we will look at a program in Excel VBA that loops through the entire first column and colors all values that are lower than a certain value. Place a command button on your worksheet and add the following code lines:

Pandas iterate row and update, The list of columns and the types in those columns the schema. age, row. Pass in a number and Pandas will print out the specified number of rows as shown in the In addition to iterrows, Pandas also has an useful function itertuples(). But it comes in handy when you want to iterate over columns of your choosing only. Dataframe class provides a member function iteritems () which gives an iterator that can be utilized to iterate over all the columns of a data frame. For every column in the Dataframe it returns an iterator to the tuple containing the column name and its contents as series.

Comments
  • please do add some sample data too which matches the counts
  • What is error here?
  • sorry i was not printing
  • @jezrael please dont vote me down , was not using print
  • @anky_91 sorry was not using print
  • can you explain why ` for i in range(len(x)): df[x[i]].value_counts()` did not work