10 examples of 'pandas convert column to datetime' in Python

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def _is_datetime(s):
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    if is_datetime64_any_dtype(s):
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        return True
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    try:
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        if is_object_dtype(s):
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            pd.to_datetime(s, infer_datetime_format=True)
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            return True
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    except Exception:  # pylint: disable=broad-except
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        pass
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    return False

30
def converter(df: DataFrame) -> Series:
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    return df[field_name]

552
def detect_datetime_columns(data_frame):
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    """
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    Given a data frame traverse the columns and those that have type "string"
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    try to see if it is of type datetime. If so, apply the translation.
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    :param data_frame: Pandas dataframe to detect datetime columns
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    :return:
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    """
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    # Strip white space from all string columns and try to convert to
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    # datetime just in case
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    for x in list(data_frame.columns):
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        if data_frame[x].dtype.name == 'object':
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            # Column is a string!
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            data_frame[x] = data_frame[x].str.strip()
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            # Try the datetime conversion
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            try:
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                series = pd.to_datetime(data_frame[x],
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                                        infer_datetime_format=True)
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                # Datetime conversion worked! Update the data_frame
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                data_frame[x] = series
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            except ValueError:
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                pass
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    return data_frame

553
def to_datetime(self, dayfirst=False):
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    """
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    DEPRECATED: use :meth:`to_timestamp` instead.
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    Cast to DatetimeIndex.
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    """
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    warnings.warn("to_datetime is deprecated. Use self.to_timestamp(...)",
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                  FutureWarning, stacklevel=2)
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    return self.to_timestamp()

267
def _convert_datetime_type(self, column):
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    """The Avro timestamp logical type is used to represent
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    MySQL datetime type, and it chooses timestamp-millis or
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    timestamp-micros depending on the fsp value.
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    """
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    metadata = self._get_primary_key_metadata(column.primary_key_order)
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    metadata[AvroMetaDataKeys.DATETIME] = True
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    fsp = column.type.fsp
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    metadata.update(self._get_fsp_metadata(fsp))
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    return self._builder.create_string(), metadata

148
def localize_datetime(x):
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    if pd.isnull(x) or isinstance(x, pd.tslib.NaTType):
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        return None
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    utz_tz = pytz.timezone('UTC')
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    local_tz = pytz.timezone(utc_to_tz)
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    d = utz_tz.localize(x)
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    d = d.astimezone(local_tz)
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    # make naive.
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    return d.replace(tzinfo=None)

2
def convert_columns_dtype(df, old_dtype, new_dtype):
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    """
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    Parameters
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    ----------
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    df: pandas.DataFrame
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    old_dtype: numpy dtype
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    new_dtype: numpy dtype
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    """
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    changed = []
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    for column in df.columns:
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        if df[column].dtype == old_dtype:
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            df[column] = df[column].astype(new_dtype)
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            changed.append(column)
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    return changed

302
def to_pandas(self) -> None:
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    '''Return a Pandas Index.
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    '''
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    raise NotImplementedError('Pandas does not support a year month type, and it is ambiguous if a date proxy should be the first of the month or the last of the month.')

653
def set_index_post_series(df, index_name, drop, column_dtype):
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    df2 = df.drop("_partitions", axis=1).set_index("_index", drop=True)
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    df2.index.name = index_name
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    df2.columns = df2.columns.astype(column_dtype)
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    return df2

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def convert_input(X, columns=None, deep=False):
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    """
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    Unite data into a DataFrame.
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    Objects that do not contain column names take the names from the argument.
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    Optionally perform deep copy of the data.
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    """
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    if not isinstance(X, pd.DataFrame):
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        if isinstance(X, pd.Series):
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            X = pd.DataFrame(X, copy=deep)
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        else:
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            if columns is not None and np.size(X,1) != len(columns):
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                raise ValueError('The count of the column names does not correspond to the count of the columns')
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            if isinstance(X, list):
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                X = pd.DataFrame(X, columns=columns, copy=deep)  # lists are always copied, but for consistency, we still pass the argument
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            elif isinstance(X, (np.generic, np.ndarray)):
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                X = pd.DataFrame(X, columns=columns, copy=deep)
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            elif isinstance(X, csr_matrix):
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                X = pd.DataFrame(X.todense(), columns=columns, copy=deep)
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            else:
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                raise ValueError('Unexpected input type: %s' % (str(type(X))))
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            X = X.apply(lambda x: pd.to_numeric(x, errors='ignore'))
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    elif deep:
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        X = X.copy(deep=True)
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    return X