1

I have tried several versions but all of them throw a warning, starting with:

colName = 'age'    
df_plot[colName][df_plot[colName]>10] = 10

 SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame. See the caveats in the documentation:

http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

Based on the link from the warning then:

df_plot.loc[:, (colName, df_plot[colName]>10)] = 10

A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

Next

df_plot.loc[colName, df_plot[colName] > 10] = 10

TypeError: 'Series' objects are mutable, thus they cannot be hashed

And finally based on a stack-overflow answer also:

df_plot[colName] = df_plot[colName].apply(lambda x: [y if y <= 10 else 10 for y in x])


TypeError: 'float' object is not iterable

What am I doing wrong here?

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3
  • 2
    df_plot.loc[df_plot[colName]>10, colName] = 10 ? Commented Oct 29, 2019 at 9:09
  • First option doesn't even throw any warnings for me. Commented Oct 29, 2019 at 9:12
  • Concerning the last option: if you drop the list comprehension, it should work fine, i.e.: df_plot[colName] = df_plot[colName].apply(lambda x: x if x <= 10 else 10) Commented Oct 29, 2019 at 9:56

3 Answers 3

Reset to default
2

There's an easier way to clip values:

df_plot[colName] = df_plot[colName].clip(upper=10)
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1

Your first attempt seems correct to me, and works without warnings on my machine.

df = pd.DataFrame({'age':range(5,15), 'size':range(10)})
colName = 'age'
df[colName][df[colName]>10] = 10
print(df)

Output:

   age  size
0    5     0
1    6     1
2    7     2
3    8     3
4    9     4
5   10     5
6   10     6
7   10     7
8   10     8
9   10     9
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4 Comments

Interesting, your minimal version works on my system without warning but why does it throw a warning with my "real" data frame ...? EDIT: My column is of type <class 'pandas.core.series.Series'> - very strange (it's an import from a SQL DB)
@lordy: The warning occurs only in case df_plot is already a copy of a slice. This depends on how df_plot is defined. Maybe, before your posted commands, you slice another DataFrame for example by using some command like this: df_plot = df_original[['age', 'height', 'weight']]. Then the warning is raise. If instead you do df_plot = df_original[['age', 'height', 'weight']].copy() the warning will disappear.
@brexi: you are right - I am doing that as I select only a subset of columns for plotting but the .copy() does not remove the warning ...
that is bizarre. Could you maybe post a few more lines of code leading up to what you already posted?
0

Have you tried numpy.where()? Let me use @Zvika's sample df.

>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame({'age':range(5,15), 'size':range(10)})
>>> df
   age  size
0    5     0
1    6     1
2    7     2
3    8     3
4    9     4
5   10     5
6   11     6
7   12     7
8   13     8
9   14     9

>>> df['age'] = np.where(df['age']>10, [10],df['age'])
>>> df
   age  size
0    5     0
1    6     1
2    7     2
3    8     3
4    9     4
5   10     5
6   10     6
7   10     7
8   10     8
9   10     9
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