## Get total of Pandas 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?