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Error in getting specific row from python pandas


Error in getting specific row from python pandas

By : Sylar
Date : November 21 2020, 09:01 AM
this one helps. You don't have that label in your index, it's in your first column the following should work:
code :
df.loc[df['Unnamed: 1'] == 'WOC(kg/ha)']
df.set_index('Unnamed: 1', inplace=True)


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Python & Pandas: Strange behavior when Pandas plot histogram to a specific ax

Python & Pandas: Strange behavior when Pandas plot histogram to a specific ax


By : Wilson Cifuentes
Date : March 29 2020, 07:55 AM
will help you The problem is that pandas determines which is the active figure by using gcf() to get the "current figure". When you create several figures in a row, the "current figure" is the last one created. But you are trying to plot to an earlier one, which causes a mismatch.
However, as you can see on line 2954 of the source you linked to, pandas will look for an (undocumented) figure argument. So you can make it work by doing df['speed'].hist(ax=ax2, figure=fig2). A comment in the pandas source notes that this is a "hack until the plotting interface is a bit more unified", so I wouldn't rely on it for anything too critical.
code :
fig1, ax1 = plt.subplots(figsize=(4,3))
fig2, ax2 = plt.subplots(figsize=(4,3))
fig3, ax3 = plt.subplots(figsize=(4,3))

something.hist(ax=ax1)
something.hist(ax=ax2)
something.hist(ax=ax3)
fig1, ax1 = plt.subplots(figsize=(4,3))
something.hist(ax=ax1)

fig2, ax2 = plt.subplots(figsize=(4,3))
something.hist(ax=ax2)

fig3, ax3 = plt.subplots(figsize=(4,3))
something.hist(ax=ax3)
Python - Pandas - Key Error during dropna call for specific subsets

Python - Pandas - Key Error during dropna call for specific subsets


By : Breakfast
Date : March 29 2020, 07:55 AM
I hope this helps you . My goal: I wish to drop rows who have NaN in specific columns. I will allow NaN to exist on some columns but not others. English Example: If value of 'detail_age' in a row is NaN, I want to remove that row. , Demo:
code :
In [360]: df
Out[360]:
      A     B     C   D
0   1.0   2.0   NaN   4
1   5.0   NaN   7.0   8
2   NaN  10.0  11.0  12
3  13.0  14.0  15.0  16

In [362]: df = df.dropna(subset=df.columns[[1,2]], how='any')

In [363]: df
Out[363]:
      A     B     C   D
2   NaN  10.0  11.0  12
3  13.0  14.0  15.0  16
In [370]: df.dropna(subset=['B','C'], how='any')
Out[370]:
      A     B     C   D
2   NaN  10.0  11.0  12
3  13.0  14.0  15.0  16
Python Pandas style function to highlight specific columns under specific condition

Python Pandas style function to highlight specific columns under specific condition


By : Sakthi Vel
Date : March 29 2020, 07:55 AM
I hope this helps . You can use apply() method with axis=1 argument to apply your style row-wise:
code :
df = pd.DataFrame({'C1': [0,6,7], 'C2':[1,4,5]})

def color_red(s):
    color = 'color: red' if s.iloc[0] > s.iloc[1] else 'color: black'
    colors = [color] + [''] * (s.size - 1)
    return colors

s = df.style.apply(color_red, axis=1)
python pandas. How to update the extraction results of a specific row to other columns of the original specific row?

python pandas. How to update the extraction results of a specific row to other columns of the original specific row?


By : user2452389
Date : March 29 2020, 07:55 AM
I hope this helps you . Change regex pattern for extract values between 【】, only once create boolean mask m and last use DataFrame.loc for select by mask and column:
code :
m = df['B'] == ""
df.loc[m, "B"] = df.loc[m,"A"].str.extract(r"【([A-Z][0-9]+)】",expand=False)

print (df)
               A        B
0  xxxx【A001-01】  A001-01
1     xxxx【A002】     A002
2  xxxx【A003-01】  A003-01
Python 3 Pandas Error: pandas.parser.CParserError: Error tokenizing data. C error: Expected 11 fields in line 5, saw 13

Python 3 Pandas Error: pandas.parser.CParserError: Error tokenizing data. C error: Expected 11 fields in line 5, saw 13


By : Victor Piolin
Date : March 29 2020, 07:55 AM
To fix the issue you can do I checked out this answer as I am having a similar problem. , Solution is to use pandas built-in delimiter "sniffing".
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