# pandas.DataFrame.clip_lower

`DataFrame.``clip_lower`(threshold, axis=None, inplace=False)

  Trim values below a given threshold.

  Deprecated since version 0.24.0: Use clip(lower=threshold) instead.

  Elements below the threshold will be changed to match the threshold value(s). Threshold can be a single value or an array, in the latter case it performs the truncation element-wise.

  Parameters:

  threshold : numeric or array-like

  > Minimum value allowed. All values below threshold will be set to this value.

  > - float : every value is compared to threshold.
  > - array-like : The shape of threshold should match the object it’s compared to. When self is a Series, threshold should be the length. When self is a DataFrame, threshold should 2-D and the same shape as self for `axis=None`, or 1-D and the same length as the axis being compared.

  axis : {0 or ‘index’, 1 or ‘columns’}, default 0

  > Align self with threshold along the given axis.

  inplace : bool, default False

  > Whether to perform the operation in place on the data.

  > New in version 0.21.0.

  Returns:

  Series or DataFrame

  > Original data with values trimmed.

  See also

  `Series.clip`
    General purpose method to trim Series values to given threshold(s).
  `DataFrame.clip`
    General purpose method to trim DataFrame values to given threshold(s).

  Examples

  Series single threshold clipping:

      >>> s = pd.Series([5, 6, 7, 8, 9])
      >>> s.clip(lower=8)
      0    8
      1    8
      2    8
      3    8
      4    9
      dtype: int64

  Series clipping element-wise using an array of thresholds. threshold should be the same length as the Series.

      >>> elemwise_thresholds = [4, 8, 7, 2, 5]
      >>> s.clip(lower=elemwise_thresholds)
      0    5
      1    8
      2    7
      3    8
      4    9
      dtype: int64

  DataFrames can be compared to a scalar.

      >>> df = pd.DataFrame({"A": [1, 3, 5], "B": [2, 4, 6]})
      >>> df
         A  B
      0  1  2
      1  3  4
      2  5  6

      >>> df.clip(lower=3)
         A  B
      0  3  3
      1  3  4
      2  5  6

  Or to an array of values. By default, threshold should be the same shape as the DataFrame.

      >>> df.clip(lower=np.array([[3, 4], [2, 2], [6, 2]]))
         A  B
      0  3  4
      1  3  4
      2  6  6

  Control how threshold is broadcast with axis. In this case threshold should be the same length as the axis specified by axis.

      >>> df.clip(lower=[3, 3, 5], axis='index')
         A  B
      0  3  3
      1  3  4
      2  5  6

      >>> df.clip(lower=[4, 5], axis='columns')
         A  B
      0  4  5
      1  4  5
      2  5  6

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