pandas multi index changing float to string

It may be easier, as has been mentioned in other posts, to just reset your index, change dtypes, and set a new index.

np.random.seed(0)
tuples = list(zip( * [
   ['bar', 'bar', 'baz', 'baz',
      'foo', 'foo', 'qux', 'qux'
   ],
   [1.0, 2.0, 1.0, 2.0,
      1.0, 2.0, 1.0, 2.0
   ]
]))

idx = pd.MultiIndex.from_tuples(tuples, names = ['first', 'second'])
df = pd.DataFrame(np.random.randn(8, 2), index = idx, columns = ['A', 'B'])

print(df)
print(df.index.get_level_values("second").dtype)

Output:

                         A B
                         first second
                         bar 1.0 1.764052 0.400157
                         2.0 0.978738 2.240893
                         baz 1.0 1.867558 - 0.977278
                         2.0 0.950088 - 0.151357
                         foo 1.0 - 0.103219 0.410599
                         2.0 0.144044 1.454274
                         qux 1.0 0.761038 0.121675
                         2.0 0.443863 0.333674
                         float64

Now, reset the index, change dtype, and set new index.

df = df.reset_index()
df["second"] = df["second"].astype(int).astype(str)
df = df.set_index(["first", "second"])

print(df)
print(df.index.get_level_values("second").dtype)

Suggestion : 2

Last Updated : 20 Aug, 2020

Syntax :

DataFrame.astype(dtype, copy = True, errors = ’raise’, ** kwargs)

Syntax:

Series.map(arg, na_action = None)

Suggestion : 3

I have a panda's data frame with a anycodings_pandas multiindex of three levels. When I anycodings_pandas concatenated the two dataframes, it turned anycodings_pandas the lowest index into a float where it anycodings_pandas should be a string. ,If you provide sample code to create anycodings_python your dataframe, I can extend it to 3 anycodings_python levels, or you can work it out. :),It may be easier, as has been mentioned anycodings_python in other posts, to just reset your anycodings_python index, change dtypes, and set a new anycodings_python index.,In general, I have found manipulating anycodings_python multiindexes (indices?) to be sometimes anycodings_python worth the bother, and sometimes not. anycodings_python Changing levels gets verbose. If you're anycodings_python dedicated to the cause, this works:

1._

pd.__version__ '0.15.2'

I tried to replace the the .0 with nothing anycodings_pandas using

idx = str(dfmaster_stats.index.levels[2]).replace('.0', '')

and assigning it to the dataframe, but I anycodings_pandas get this error

TypeError: 'FrozenList'
does not support mutable operations.

gives me this error

TypeError: 'Index'
object is not callable

It may be easier, as has been mentioned anycodings_python in other posts, to just reset your anycodings_python index, change dtypes, and set a new anycodings_python index.

np.random.seed(0)
tuples = list(zip( * [
   ['bar', 'bar', 'baz', 'baz',
      'foo', 'foo', 'qux', 'qux'
   ],
   [1.0, 2.0, 1.0, 2.0,
      1.0, 2.0, 1.0, 2.0
   ]
]))

idx = pd.MultiIndex.from_tuples(tuples, names = ['first', 'second'])
df = pd.DataFrame(np.random.randn(8, 2), index = idx, columns = ['A', 'B'])

print(df)
print(df.index.get_level_values("second").dtype)

Output:

                         A B
                         first second
                         bar 1.0 1.764052 0.400157
                         2.0 0.978738 2.240893
                         baz 1.0 1.867558 - 0.977278
                         2.0 0.950088 - 0.151357
                         foo 1.0 - 0.103219 0.410599
                         2.0 0.144044 1.454274
                         qux 1.0 0.761038 0.121675
                         2.0 0.443863 0.333674
                         float64

Now, reset the index, change dtype, and anycodings_python set new index.

df = df.reset_index()
df["second"] = df["second"].astype(int).astype(str)
df = df.set_index(["first", "second"])

print(df)
print(df.index.get_level_values("second").dtype)

Suggestion : 4

Pandas Multi Index Changing Float to String,Pandas counting string pattern and appending to column multi index,Pandas dataframe with MultiIndex: check if string is contained in index level,Python Pandas Changing Column String Values to Float values in a new column

It may be easier, as has been mentioned in other posts, to just reset your index, change dtypes, and set a new index.

np.random.seed(0)
tuples = list(zip( * [
   ['bar', 'bar', 'baz', 'baz',
      'foo', 'foo', 'qux', 'qux'
   ],
   [1.0, 2.0, 1.0, 2.0,
      1.0, 2.0, 1.0, 2.0
   ]
]))

idx = pd.MultiIndex.from_tuples(tuples, names = ['first', 'second'])
df = pd.DataFrame(np.random.randn(8, 2), index = idx, columns = ['A', 'B'])

print(df)
print(df.index.get_level_values("second").dtype)

Output:

                         A B
                         first second
                         bar 1.0 1.764052 0.400157
                         2.0 0.978738 2.240893
                         baz 1.0 1.867558 - 0.977278
                         2.0 0.950088 - 0.151357
                         foo 1.0 - 0.103219 0.410599
                         2.0 0.144044 1.454274
                         qux 1.0 0.761038 0.121675
                         2.0 0.443863 0.333674
                         float64

Now, reset the index, change dtype, and set new index.

df = df.reset_index()
df["second"] = df["second"].astype(int).astype(str)
df = df.set_index(["first", "second"])

print(df)
print(df.index.get_level_values("second").dtype)

Suggestion : 5

This page illustrates how to convert a float column to the string data type in a pandas DataFrame in the Python programming language.,The following Python programming syntax demonstrates how to convert one single column from the float class to a character string.,This example illustrates how to convert all columns of a pandas DataFrame from float to the string data type.,In the previous examples, we have used the astype function to convert our float columns to the string data type.

import pandas as pd # Load pandas

data = pd.DataFrame({
   'x1': [1.1, 2.2, 3.3, 4.4, 5.5],
   # Create pandas DataFrame 'x2': [1.5, 2.5, 3.5, 4.5, 5.5],
   'x3': [0.1, 0.2, 0.3, 0.4, 0.5]
})
print(data) # Print pandas DataFrame

print(data.dtypes) # Check data types of columns
# x1 float64
# x2 float64
# x3 float64
# dtype: object

data_new1 = data.copy() # Create copy of DataFrame
data_new1['x1'] = data_new1['x1'].astype(str) # Transform float to string

print(data_new1.dtypes) # Check data types of columns
# x1 object
# x2 float64
# x3 float64
# dtype: object

data_new2 = data.copy() # Create copy of DataFrame
data_new2 = data_new2.astype({
   'x2': str,
   'x3': str
}) # Transform multiple floats to string

Suggestion : 6

Note that the columns of a DataFrame are an index, so that using rename_axis with the columns argument will change the name of that index.,The rename() method is used to rename the labels of a MultiIndex, and is typically used to rename the columns of a DataFrame. The columns argument of rename allows a dictionary to be specified that includes only the columns you wish to rename.,When working with an Index object directly, rather than via a DataFrame, Index.set_names() can be used to change the names.,Indexing will work even if the data are not sorted, but will be rather inefficient (and show a PerformanceWarning). It will also return a copy of the data rather than a view:

In[1]: arrays = [
      ...: ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
      ...: ["one", "two", "one", "two", "one", "two", "one", "two"],
      ...:
   ]
   ...:

   In[2]: tuples = list(zip( * arrays))

In[3]: tuples
Out[3]: [('bar', 'one'),
   ('bar', 'two'),
   ('baz', 'one'),
   ('baz', 'two'),
   ('foo', 'one'),
   ('foo', 'two'),
   ('qux', 'one'),
   ('qux', 'two')
]

In[4]: index = pd.MultiIndex.from_tuples(tuples, names = ["first", "second"])

In[5]: index
Out[5]:
   MultiIndex([('bar', 'one'),
         ('bar', 'two'),
         ('baz', 'one'),
         ('baz', 'two'),
         ('foo', 'one'),
         ('foo', 'two'),
         ('qux', 'one'),
         ('qux', 'two')
      ],
      names = ['first', 'second'])

In[6]: s = pd.Series(np.random.randn(8), index = index)

In[7]: s
Out[7]:
   first second
bar one 0.469112
two - 0.282863
baz one - 1.509059
two - 1.135632
foo one 1.212112
two - 0.173215
qux one 0.119209
two - 1.044236
dtype: float64

In[8]: iterables = [
   ["bar", "baz", "foo", "qux"],
   ["one", "two"]
]

In[9]: pd.MultiIndex.from_product(iterables, names = ["first", "second"])
Out[9]:
   MultiIndex([('bar', 'one'),
         ('bar', 'two'),
         ('baz', 'one'),
         ('baz', 'two'),
         ('foo', 'one'),
         ('foo', 'two'),
         ('qux', 'one'),
         ('qux', 'two')
      ],
      names = ['first', 'second'])

In[10]: df = pd.DataFrame(
      ....: [
         ["bar", "one"],
         ["bar", "two"],
         ["foo", "one"],
         ["foo", "two"]
      ],
      ....: columns = ["first", "second"],
      ....: )
   ....:

   In[11]: pd.MultiIndex.from_frame(df)
Out[11]:
   MultiIndex([('bar', 'one'),
         ('bar', 'two'),
         ('foo', 'one'),
         ('foo', 'two')
      ],
      names = ['first', 'second'])

In[12]: arrays = [
      ....: np.array(["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"]),
      ....: np.array(["one", "two", "one", "two", "one", "two", "one", "two"]),
      ....:
   ]
   ....:

   In[13]: s = pd.Series(np.random.randn(8), index = arrays)

In[14]: s
Out[14]:
   bar one - 0.861849
two - 2.104569
baz one - 0.494929
two 1.071804
foo one 0.721555
two - 0.706771
qux one - 1.039575
two 0.271860
dtype: float64

In[15]: df = pd.DataFrame(np.random.randn(8, 4), index = arrays)

In[16]: df
Out[16]:
   0 1 2 3
bar one - 0.424972 0.567020 0.276232 - 1.087401
two - 0.673690 0.113648 - 1.478427 0.524988
baz one 0.404705 0.577046 - 1.715002 - 1.039268
two - 0.370647 - 1.157892 - 1.344312 0.844885
foo one 1.075770 - 0.109050 1.643563 - 1.469388
two 0.357021 - 0.674600 - 1.776904 - 0.968914
qux one - 1.294524 0.413738 0.276662 - 0.472035
two - 0.013960 - 0.362543 - 0.006154 - 0.923061

In[17]: df.index.names
Out[17]: FrozenList([None, None])

In[18]: df = pd.DataFrame(np.random.randn(3, 8), index = ["A", "B", "C"], columns = index)

In[19]: df
Out[19]:
   first bar baz foo qux
second one two one two one two one two
A 0.895717 0.805244 - 1.206412 2.565646 1.431256 1.340309 - 1.170299 - 0.226169
B 0.410835 0.813850 0.132003 - 0.827317 - 0.076467 - 1.187678 1.130127 - 1.436737
C - 1.413681 1.607920 1.024180 0.569605 0.875906 - 2.211372 0.974466 - 2.006747

In[20]: pd.DataFrame(np.random.randn(6, 6), index = index[: 6], columns = index[: 6])
Out[20]:
   first bar baz foo
second one two one two one two
first second
bar one - 0.410001 - 0.078638 0.545952 - 1.219217 - 1.226825 0.769804
two - 1.281247 - 0.727707 - 0.121306 - 0.097883 0.695775 0.341734
baz one 0.959726 - 1.110336 - 0.619976 0.149748 - 0.732339 0.687738
two 0.176444 0.403310 - 0.154951 0.301624 - 2.179861 - 1.369849
foo one - 0.954208 1.462696 - 1.743161 - 0.826591 - 0.345352 1.314232
two 0.690579 0.995761 2.396780 0.014871 3.357427 - 0.317441

Suggestion : 7

In this article, I will explain how to convert single column or multiple columns to string type in pandas DataFrame, here, I will demonstrate using DataFrame.astype(str), DataFrame.values.astype(str), DataFrame.apply(str), DataFrame.map(str) and DataFrame.applymap(str) methods to covert any type to string type.,Use pandas DataFrame.astype() function to convert a column from int to string, you can apply this on a specific column or on an entire DataFrame.,You can also convert multiple columns to string by sending dict of column name -> data type to astype() method. The below example converts column Fee from int to string and Discount from float to string dtype.,If you are in a hurry, below are some of the quick examples of how to convert column to string type in Pandas DataFrame. You can apply these to convert from/to any type in Pandas.

1._

# Below are quick example

# Convert "Fee"
from int to string
df = df.astype({
   'Fee': 'string'
})

# Using Series.astype() to convert to string
df["Fee"] = df["Fee"].values.astype('string')

# Multiple columns string conversion
df = pd.DataFrame(technologies)
df = df.astype({
   'Fee': 'string',
   'Discount': 'string'
})

# Multiple columns string conversion
df = pd.DataFrame(technologies)
df[['Fee', 'Discount']] = df[['Fee', 'Discount']].astype(str)

# Multiple columns string conversion
df["Fee"] = df["Fee"].astype(str)
df["Discount"] = df["Discount"].astype(str)

# Using apply(str) method
df["Fee"] = df["Fee"].apply(str)

# Using apply(str) with lambda
function
df["Fee"] = df["Fee"].apply(lambda x: str(x))

# Using map(str) method
df['Fee'] = df["Fee"].map(str)

# Convert entire DataFrame to string
df = df.applymap(str)

# Convert entire DataFrame to string
df = df.astype(str)

Now, let’s see a detailed example. first, create a Pandas DataFrame with a few rows and columns, and execute and validate the results. Our DataFrame contains column names Courses, Fee, Duration, and Discount.

import pandas as pd
import numpy as np
technologies = ({
   'Courses': ["Spark", "PySpark", "Hadoop", "Python", "Pandas", "Hadoop", "Spark"],
   'Fee': [22000, 25000, 23000, 24000, 26000, 25000, 25000],
   'Duration': ['30day', '50days', '55days', '40days', '60days', '35day', '55days'],
   'Discount': [1000, 2300, 1000, 1200, 2500, 1300, 1400]
})
df = pd.DataFrame(technologies)
print(df)
print(df.dtypes)

Yields below output.

Courses Fee Duration Discount
0 Spark 22000 30 day 1000
1 PySpark 25000 50 days 2300
2 Hadoop 23000 55 days 1000
3 Python 24000 40 days 1200
4 Pandas 26000 60 days 2500
5 Hadoop 25000 35 day 1300
6 Spark 25000 55 days 1400

Courses object
Fee int64
Duration object
Discount int64
dtype: object

5._

Courses object
Fee string
Duration object
Discount int64
dtype: object

You can also use Series.astype() to convert a specific column. Since each column on DataFrame is pandas Series, I will get the column from DataFrame as Series and use astype() function. In the below example df.Fee or df['Fee'] returns Series object.

# Using Series.astype() to convert column to string
df["Fee"] = df["Fee"].values.astype('string')
print(df.dtypes)