blocks|key|216134|text|这样如何：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|216135|d+=+d.applymap(lambda+x:+np.nan+if+isinstance(x,+basestring)+and+x.isspace()+else+x)|code-block|syntax|javascript|216136|applymap函数将一个函数应用于数据帧的每个单元。|offset|length|style|CODE|216137|entityMap^0|0|0|0|8|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Q|8|@$I|R|J|S|K|L]]|9|@]|A|$]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

How about:

<pre><code>d = d.applymap(lambda x: np.nan if isinstance(x, basestring) and x.isspace() else x)
</code></pre>

The <code>applymap</code> function applies a function to every cell of the dataframe.

blocks|key|434241|text|我这样做了：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|434242|df+=+df.apply(lambda+x:+x.str.strip()).replace('',+np.nan)|code-block|syntax|javascript|434243|或|434244|df+=+df.apply(lambda+x:+x.str.strip()+if+isinstance(x,+str)+else+x).replace('',+np.nan)|434245|您可以去掉所有字符串，然后用np.nan替换空字符串。|offset|length|style|CODE|434246|entityMap^0|0|0|0|0|E|6|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|T|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|U|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|V|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|W|8|@$M|X|N|Y|O|P]]|9|@]|A|$]]|$1|Q|3|-4|5|6|7|Z|8|@]|9|@]|A|$]]]|R|$]]

I did this:
<pre><code>df = df.apply(lambda x: x.str.strip()).replace('', np.nan)
</code></pre>
or
<pre><code>df = df.apply(lambda x: x.str.strip() if isinstance(x, str) else x).replace('', np.nan)
</code></pre>
You can strip all str, then replace empty str with <code>np.nan</code>.

blocks|key|216212|text|对于针对单个值检查相等性的非常快速且简单的解决方案，可以使用mask方法。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|216213|df.mask(df+==+'+')|code-block|syntax|javascript|216214|entityMap^0|U|4|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|P|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Q|8|@]|D|@]|E|$]]]|L|$]]

For a very fast and simple solution where you check equality against a single value, you can use the <code>mask</code> method.

<pre><code>df.mask(df == ' ')
</code></pre>

blocks|key|434324|text|如果要从CSV文件中导出数据，可以像下面这样简单：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|434325|df+=+pd.read_csv(file_csv,+na_values='+')|code-block|syntax|javascript|434326|这将创建数据框并将空白值替换为Na|434327|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

If you are exporting the data from the CSV file it can be as simple as this :

<pre><code>df = pd.read_csv(file_csv, na_values=' ')
</code></pre>

This will create the data frame as well as replace blank values as Na

blocks|key|434355|text|您也可以使用筛选器来执行此操作。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|434356|df+=+PD.DataFrame([
++++[-0.532681,+'foo',+0],
++++[1.490752,+'bar',+1],
++++[-1.387326,+'foo',+2],
++++[0.814772,+'baz',+'+'],+++++
++++[-0.222552,+'+++',+4],
++++[-1.176781,++'qux',+'++'])
++++df[df=='']='nan'
++++df=df.astype(float)|code-block|syntax|javascript|434357|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

you can also use a filter to do it.

<pre><code>df = PD.DataFrame([
 [-0.532681, 'foo', 0],
 [1.490752, 'bar', 1],
 [-1.387326, 'foo', 2],
 [0.814772, 'baz', ' '], 
 [-0.222552, ' ', 4],
 [-1.176781, 'qux', ' '])
 df[df=='']='nan'
 df=df.astype(float)
</code></pre>

blocks|key|216293|text|print(df.isnull().sum())+#+check+numbers+of+null+value+in+each+column

modifiedDf=df.fillna("NaN")+#+Replace+empty/null+values+with+"NaN"

#+modifiedDf+=+fd.dropna()+#+Remove+rows+with+empty+values

print(modifiedDf.isnull().sum())+#+check+numbers+of+null+value+in+each+column|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|216294|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>print(df.isnull().sum()) # check numbers of null value in each column

modifiedDf=df.fillna("NaN") # Replace empty/null values with "NaN"

# modifiedDf = fd.dropna() # Remove rows with empty values

print(modifiedDf.isnull().sum()) # check numbers of null value in each column
</code></pre>

blocks|key|434427|text|这不是一个优雅的解决方案，但似乎有效的是将其保存为XLSX，然后再将其导入。这个页面上的其他解决方案对我不起作用，不知道为什么。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|434428|data.to_excel(filepath,+index=False)
data+=+pd.read_excel(filepath)|code-block|syntax|javascript|434429|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

This is not an elegant solution, but what does seem to work is saving to XLSX and then importing it back. The other solutions on this page did not work for me, unsure why.

<pre><code>data.to_excel(filepath, index=False)
data = pd.read_excel(filepath)
</code></pre>

blocks|key|434458|text|这对我很有效。当我导入csv文件时，我添加了na_values+=‘’。空格不包括在默认的NaN值中。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|434459|df=+pd.read_csv(filepath,na_values+=+'+')|code-block|syntax|javascript|434460|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

This worked for me.
When I import my csv file I added na_values = ' '. Spaces are not included in the default NaN values.
<pre><code>df= pd.read_csv(filepath,na_values = ' ')
</code></pre>

blocks|key|434479|text|这应该是可行的|type|unstyled|depth|inlineStyleRanges|entityRanges|data|434480|df.loc[df.Variable+==+'',+'Variable']+=+'Value'|code-block|syntax|javascript|434481|或|434482|df.loc[df.Variable1+==+'',+'Variable2']+=+'Value'|434483|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

This should work
<pre><code>df.loc[df.Variable == '', 'Variable'] = 'Value'
</code></pre>
or
<pre><code>df.loc[df.Variable1 == '', 'Variable2'] = 'Value'
</code></pre>

I want to find all values in a Pandas dataframe that contain whitespace (any arbitrary amount) and replace those values with NaNs.

Any ideas how this can be improved?

Basically I want to turn this:

<pre class="lang-none prettyprint-override"><code> A B C
2000-01-01 -0.532681 foo 0
2000-01-02 1.490752 bar 1
2000-01-03 -1.387326 foo 2
2000-01-04 0.814772 baz 
2000-01-05 -0.222552 4
2000-01-06 -1.176781 qux 
</code></pre>

Into this:

<pre class="lang-none prettyprint-override"><code> A B C
2000-01-01 -0.532681 foo 0
2000-01-02 1.490752 bar 1
2000-01-03 -1.387326 foo 2
2000-01-04 0.814772 baz NaN
2000-01-05 -0.222552 NaN 4
2000-01-06 -1.176781 qux NaN
</code></pre>

I've managed to do it with the code below, but man is it ugly. It's not Pythonic and I'm sure it's not the most efficient use of pandas either. I loop through each column and do boolean replacement against a column mask generated by applying a function that does a regex search of each value, matching on whitespace.

<pre><code>for i in df.columns:
 df[i][df[i].apply(lambda i: True if re.search('^\s*$', str(i)) else False)]=None
</code></pre>

It could be optimized a bit by only iterating through fields that could contain empty strings:

<pre><code>if df[i].dtype == np.dtype('object')
</code></pre>

But that's not much of an improvement

And finally, this code sets the target strings to None, which works with Pandas' functions like <code>fillna()</code>, but it would be nice for completeness if I could actually insert a <code>NaN</code> directly instead of <code>None</code>.

Replacing blank values (white space) with NaN in pandas

我希望在Pandas数据帧中找到包含空格(任意大小)的所有值，并将这些值替换为NaNs。有什么想法可以改进这一点吗？基本上，我想把这个变成：                   A    B    C2000-01-01 -0.532681  foo    02000-01-02  1.490752  bar    1...

问在pandas中用NaN替换空白值(空白)
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