Adding new column to existing DataFrame in Python pandas

Question

I have the following indexed DataFrame with named columns and rows not- continuous numbers:

          a         b         c         d
2  0.671399  0.101208 -         


        

Answer 1

I would like to add a new column, 'e', to the existing data frame and do not change anything in the data frame. (The series always got the same length as a dataframe.)

I assume that the index values in e match those in df1.

The easiest way to initiate a new column named e, and assign it the values from your series e:

df['e'] = e.values

assign (Pandas 0.16.0+)

As of Pandas 0.16.0, you can also use assign, which assigns new columns to a DataFrame and returns a new object (a copy) with all the original columns in addition to the new ones.

df1 = df1.assign(e=e.values)

As per this example (which also includes the source code of the assign function), you can also include more than one column:

df = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
>>> df.assign(mean_a=df.a.mean(), mean_b=df.b.mean())
   a  b  mean_a  mean_b
0  1  3     1.5     3.5
1  2  4     1.5     3.5

In context with your example:

np.random.seed(0)
df1 = pd.DataFrame(np.random.randn(10, 4), columns=['a', 'b', 'c', 'd'])
mask = df1.applymap(lambda x: x <-0.7)
df1 = df1[-mask.any(axis=1)]
sLength = len(df1['a'])
e = pd.Series(np.random.randn(sLength))

>>> df1
          a         b         c         d
0  1.764052  0.400157  0.978738  2.240893
2 -0.103219  0.410599  0.144044  1.454274
3  0.761038  0.121675  0.443863  0.333674
7  1.532779  1.469359  0.154947  0.378163
9  1.230291  1.202380 -0.387327 -0.302303

>>> e
0   -1.048553
1   -1.420018
2   -1.706270
3    1.950775
4   -0.509652
dtype: float64

df1 = df1.assign(e=e.values)

>>> df1
          a         b         c         d         e
0  1.764052  0.400157  0.978738  2.240893 -1.048553
2 -0.103219  0.410599  0.144044  1.454274 -1.420018
3  0.761038  0.121675  0.443863  0.333674 -1.706270
7  1.532779  1.469359  0.154947  0.378163  1.950775
9  1.230291  1.202380 -0.387327 -0.302303 -0.509652

The description of this new feature when it was first introduced can be found here.

Answer 2

1. First create a python's list_of_e that has relevant data.
2. Use this: df['e'] = list_of_e

Answer 3

Let me just add that, just like for hum3, .loc didn't solve the SettingWithCopyWarning and I had to resort to df.insert(). In my case false positive was generated by "fake" chain indexing dict['a']['e'], where 'e' is the new column, and dict['a'] is a DataFrame coming from dictionary.

Also note that if you know what you are doing, you can switch of the warning using pd.options.mode.chained_assignment = None and than use one of the other solutions given here.

Answer 4

x=pd.DataFrame([1,2,3,4,5])

y=pd.DataFrame([5,4,3,2,1])

z=pd.concat([x,y],axis=1)

Answer 5

One thing to note, though, is that if you do

df1['e'] = Series(np.random.randn(sLength), index=df1.index)

this will effectively be a left join on the df1.index. So if you want to have an outer join effect, my probably imperfect solution is to create a dataframe with index values covering the universe of your data, and then use the code above. For example,

data = pd.DataFrame(index=all_possible_values)
df1['e'] = Series(np.random.randn(sLength), index=df1.index)

Answer 6

Easiest ways:-

data['new_col'] = list_of_values

data.loc[ : , 'new_col'] = list_of_values

This way you avoid what is called chained indexing when setting new values in a pandas object. Click here to read further.