How to subset rows of a dataframe that have values lower than a negative number in one column?-CodePudding

I have a dataframe of 51077 rows × 4 columns. I need to subset a dataframe with the rows with values > 0.3 and < -0.3 in the third column.

I used the following:

df_filtered = df[np.logical_and(df["third column"] > 0.3, df["third column"] < -0.3)]

But the result showed only the columns'names

I also tried:

df_filtered = df.query("third column < -0.3 & third column > 0.3")

But the result was the same.

How do I resolve this?

CodePudding user response：

You can also use between and reverse the result:

df_filtered = df[~df['third_column'].between(-0.3, 0.3)]

Example:

>>> df
   third_column
0     -0.190030
1     -0.205187
2     -0.066776
3     -0.264480
4      0.064962
5      0.024708
6     -0.354629  # Want to keep
7     -0.180228
8      0.261640
9      0.315986  # Want to keep

>>>  df[~df['third_column'].between(-0.3, 0.3)]
   third_column
6     -0.354629
9      0.315986

CodePudding user response：

You almost got it:

df_filtered = df.loc[(df['third column'] > 0.3) | (df['third column'] < -0.3)]

df_filtered = df[(df['third column'] > 0.3) | (df['third column'] < -0.3)]