Get total of Pandas column

pandas sum one column
pandas sum columns by name
pandas add total column
pandas sum column with condition
how to sum a column in python
pandas sum two columns
pandas sum specific rows
pandas sum multiple rows

Target

I have a Pandas data frame, as shown below, with multiple columns and would like to get the total of column, MyColumn.


Data Frame - df:

print df

           X           MyColumn  Y              Z   
0          A           84        13.0           69.0   
1          B           76         77.0          127.0   
2          C           28         69.0           16.0   
3          D           28         28.0           31.0   
4          E           19         20.0           85.0   
5          F           84        193.0           70.0   

My attempt:

I have attempted to get the sum of the column using groupby and .sum():

Total = df.groupby['MyColumn'].sum()

print Total

This causes the following error:

TypeError: 'instancemethod' object has no attribute '__getitem__'

Expected Output

I'd have expected the output to be as followed:

319

Or alternatively, I would like df to be edited with a new row entitled TOTAL containing the total:

           X           MyColumn  Y              Z   
0          A           84        13.0           69.0   
1          B           76         77.0          127.0   
2          C           28         69.0           16.0   
3          D           28         28.0           31.0   
4          E           19         20.0           85.0   
5          F           84        193.0           70.0   
TOTAL                  319

You should use sum:

Total = df['MyColumn'].sum()
print (Total)
319

Then you use loc with Series, in that case the index should be set as the same as the specific column you need to sum:

df.loc['Total'] = pd.Series(df['MyColumn'].sum(), index = ['MyColumn'])
print (df)
         X  MyColumn      Y      Z
0        A      84.0   13.0   69.0
1        B      76.0   77.0  127.0
2        C      28.0   69.0   16.0
3        D      28.0   28.0   31.0
4        E      19.0   20.0   85.0
5        F      84.0  193.0   70.0
Total  NaN     319.0    NaN    NaN

because if you pass scalar, the values of all rows will be filled:

df.loc['Total'] = df['MyColumn'].sum()
print (df)
         X  MyColumn      Y      Z
0        A        84   13.0   69.0
1        B        76   77.0  127.0
2        C        28   69.0   16.0
3        D        28   28.0   31.0
4        E        19   20.0   85.0
5        F        84  193.0   70.0
Total  319       319  319.0  319.0

Two other solutions are with at, and ix see the applications below:

df.at['Total', 'MyColumn'] = df['MyColumn'].sum()
print (df)
         X  MyColumn      Y      Z
0        A      84.0   13.0   69.0
1        B      76.0   77.0  127.0
2        C      28.0   69.0   16.0
3        D      28.0   28.0   31.0
4        E      19.0   20.0   85.0
5        F      84.0  193.0   70.0
Total  NaN     319.0    NaN    NaN

df.ix['Total', 'MyColumn'] = df['MyColumn'].sum()
print (df)
         X  MyColumn      Y      Z
0        A      84.0   13.0   69.0
1        B      76.0   77.0  127.0
2        C      28.0   69.0   16.0
3        D      28.0   28.0   31.0
4        E      19.0   20.0   85.0
5        F      84.0  193.0   70.0
Total  NaN     319.0    NaN    NaN

Note: Since Pandas v0.20, ix has been deprecated. Use loc or iloc instead.

Get total of Pandas column, Return the sum of the values for the requested axis. This is equivalent to the method numpy.sum . Parameters. axis{index (0), columns (1)}. Axis for the function  Total = df['MyColumn'].sum() print(Total) 319. Then you use loc with Series, in that case, the index should be set as the same as the specific column you need to sum: df.loc['Total'] = pd.Series(df['MyColumn'].sum(), index = ['MyColumn']) print (df) X MyColumn Y Z. 0 A 84.0 13.0 69.0.

Another option you can go with here:

df.loc["Total", "MyColumn"] = df.MyColumn.sum()

#         X  MyColumn      Y       Z
#0        A     84.0    13.0    69.0
#1        B     76.0    77.0   127.0
#2        C     28.0    69.0    16.0
#3        D     28.0    28.0    31.0
#4        E     19.0    20.0    85.0
#5        F     84.0   193.0    70.0
#Total  NaN    319.0     NaN     NaN

You can also use append() method:

df.append(pd.DataFrame(df.MyColumn.sum(), index = ["Total"], columns=["MyColumn"]))


Update:

In case you need to append sum for all numeric columns, you can do one of the followings:

Use append to do this in a functional manner (doesn't change the original data frame):

# select numeric columns and calculate the sums
sums = df.select_dtypes(pd.np.number).sum().rename('total')

# append sums to the data frame
df.append(sums)
#         X  MyColumn      Y      Z
#0        A      84.0   13.0   69.0
#1        B      76.0   77.0  127.0
#2        C      28.0   69.0   16.0
#3        D      28.0   28.0   31.0
#4        E      19.0   20.0   85.0
#5        F      84.0  193.0   70.0
#total  NaN     319.0  400.0  398.0

Use loc to mutate data frame in place:

df.loc['total'] = df.select_dtypes(pd.np.number).sum()
df
#         X  MyColumn      Y      Z
#0        A      84.0   13.0   69.0
#1        B      76.0   77.0  127.0
#2        C      28.0   69.0   16.0
#3        D      28.0   28.0   31.0
#4        E      19.0   20.0   85.0
#5        F      84.0  193.0   70.0
#total  NaN     638.0  800.0  796.0

pandas.DataFrame.sum, You may use the following syntax to sum each column and row in Pandas Run the code in Python, and you'll get the total commission earned by each  Steps to Sum each Column and Row in Pandas DataFrame Step 1: Prepare your Data To start with an example, suppose that you prepared the following data about the commission Step 2: Create the DataFrame Next, create the DataFrame in order to capture the above data in Python: import pandas as Step

Similar to getting the length of a dataframe, len(df), the following worked for pandas and blaze:

Total = sum(df['MyColumn'])

or alternatively

Total = sum(df.MyColumn)
print Total

How to Sum each Column and Row in Pandas DataFrame, Firstly you should use sum: Total = df['MyColumn'].sum(). print (Total). 319. Then you use loc with Series, in that case, the index should be set  Get the number of rows and columns: df.shape. The shape attribute of pandas.DataFrame stores the number of rows and columns as a tuple (number of rows, number of columns). print(df.shape) # (891, 12) print(df.shape[0]) # 891 print(df.shape[1]) # 12. source: pandas_len_shape_size.py.

There are two ways to sum of a column

dataset = pd.read_csv("data.csv")

1: sum(dataset.Column_name)

2: dataset['Column_Name'].sum()

If there is any issue in this the please correct me..

Get total of Pandas column, Pandas DataFrame.sum() function is used to return the sum of the values for the requested axis by the user. If the input value is an index axis, then it will add all  Get Pandas column name By iteration – This is not the most recommended way to get the pandas column from the dataframe but It is the most familiar one. Using this technique you can easily print the python pandas columns header. for columne_name in dataframe.columns: print(columne_name)

As other option, you can do something like below

Group   Valuation   amount
    0   BKB Tube    156
    1   BKB Tube    143
    2   BKB Tube    67
    3   BAC Tube    176
    4   BAC Tube    39
    5   JDK Tube    75
    6   JDK Tube    35
    7   JDK Tube    155
    8   ETH Tube    38
    9   ETH Tube    56

Below script, you can use for above data

import pandas as pd    
data = pd.read_csv("daata1.csv")
bytreatment = data.groupby('Group')
bytreatment['amount'].sum()

Pandas DataFrame.sum(), This tutorial demonstrates how to get sum of column in a Pandas DataFrame. It includes sum() function and cumulative sum with groupby. Following my Pandas’ tips series (the last post was about Groupby Tips), I will explain how to display all columns and rows of a Pandas Dataframe. Besides that, I will explain how to show all values in a list inside a Dataframe and choose the precision of the numbers in a Dataframe.

How to get the sum of Pandas column, The sum() function is used to get the sum of the values for the requested axis. This is equivalent to the method numpy.sum. Syntax: DataFrame. How to get column names in Pandas dataframe While analyzing the real datasets which are often very huge in size, we might need to get the column names in order to perform some certain operations. Let’s discuss how to get column names in Pandas dataframe .

How to sum two columns in a pandas DataFrame in Python, sum() to count the total number of NaN values in the column col of the DataFrame . print(df). Output. A B 0 1.0  Extracting a column of a pandas dataframe ¶ df2.loc[: , "2005"] To extract a column you can also do: df2["2005"] Note that when you extract a single row or column, you get a one-dimensional object as output. That is called a pandas Series.

How to calculate the sum of every column in a NumPy array in Python, sum(axis=1) to find the sum of all rows in DataFrame ; axis=1 specifies that the sum will be done on the rows. print(df). Output. A B C 0 0  I want to get total of 'reply_id' in 'new' for every 'Id' Renaming columns in pandas. 994. Adding new column to existing DataFrame in Python pandas. 1355.

Comments
  • For an illustration of why pandas is not pythonic, look no further than the confusion over how to simply sum a column.
  • That's great :) Thanks for the explanation, may I ask what .loc does in the above example?
  • loc is for setting with enlargement.
  • at works for setting with enlargement too, see last edit.
  • Thanks, Is there any preferred method?
  • Hmmm, docs says The .loc/.ix/[] operations can perform enlargement when setting a non-existant key for that axis., so loc or ix or []. in next section is writes at may enlarge the object in-place as above if the indexer is missing. So all methods are good, but at is fastest I think.
  • How about the sum of all columns?