python pandas replacing strings in dataframe with numbers

Question

Is there anyway to use the mapping function or something better to replace values in an entire dataframe?

I only know how to perform the mapping on series.

I

Answer 1

You can build dictionary from column values itself and fill like below

x=df['Item_Type'].value_counts()
item_type_mapping={}
item_list=x.index
for i in range(0,len(item_list)):
    item_type_mapping[item_list[i]]=i

df['Item_Type']=df['Item_Type'].map(lambda x:item_type_mapping[x]) 

Answer 2

I know this is old, but adding for those searching as I was. Create a dataframe in pandas, df in this code

ip_addresses = df.source_ip.unique()
ip_dict = dict(zip(ip_addresses, range(len(ip_addresses))))

That will give you a dictionary map of the ip addresses without having to write it out.

Answer 3

To convert Strings like 'volvo','bmw' into integers first convert it to a dataframe then pass it to pandas.get_dummies()

  df  = DataFrame.from_csv("myFile.csv")
  df_transform = pd.get_dummies( df )
  print( df_transform )

Better alternative: passing a dictionary to map() of a pandas series (df.myCol) (by specifying the column brand for example)

df.brand = df.brand.map( {'volvo':0 , 'bmw':1, 'audi':2} )

Answer 4

You can also do this with pandas rename_categories. You would first need to define the column as dtype="category" e.g.

In [66]: s = pd.Series(["a","b","c","a"], dtype="category")

In [67]: s
Out[67]: 
0    a
1    b
2    c
3    a
dtype: category
Categories (3, object): [a, b, c]

and then rename them:

In [70]: s.cat.rename_categories([1,2,3])
Out[70]: 
0    1
1    2
2    3
3    1
dtype: category
Categories (3, int64): [1, 2, 3]

You can also pass a dict-like object to map the renaming, e.g.:

In [72]: s.cat.rename_categories({1: 'x', 2: 'y', 3: 'z'})

Answer 5

You can use the applymap DataFrame function to do this:

In [26]: df = DataFrame({"A": [1,2,3,4,5], "B": ['a','b','c','d','e'],
                         "C": ['b','a','c','c','d'], "D": ['a','c',7,9,2]})
In [27]: df
Out[27]:
   A  B  C  D
0  1  a  b  a
1  2  b  a  c
2  3  c  c  7
3  4  d  c  9
4  5  e  d  2

In [28]: mymap = {'a':1, 'b':2, 'c':3, 'd':4, 'e':5}

In [29]: df.applymap(lambda s: mymap.get(s) if s in mymap else s)
Out[29]:
   A  B  C  D
0  1  1  2  1
1  2  2  1  3
2  3  3  3  7
3  4  4  3  9
4  5  5  4  2

Answer 6

df.replace(to_replace=['set', 'test'], value=[1, 2]) from @Ishnark comment on accepted answer.