9 examples of 'merge multiple dataframes in pandas' in Python

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def merge(old_cols, new_cols):
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    return old_cols + new_cols

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def merge(left, right):
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    return left.merge(right, on=on, how=how, suffixes=(lsuffix, rsuffix))

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def merge_datasets(self, other):
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    """
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    This operation combines two dataframes into one new DataFrame.
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    If the operation is combining two SpatialDataFrames, the
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    geometry_type must match.
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    """
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    if isinstance(other, SpatialDataFrame) and \
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        other.geometry_type == self.geometry_type:
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        return pd.concat(objs=[self, other], axis=0)
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    elif isinstance(other, DataFrame):
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        return pd.concat(objs=[self, other], axis=0)
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    elif isinstance(other, Series):
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        self['merged_datasets'] = other
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    elif isinstance(other, SpatialDataFrame) and \
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         other.geometry_type != self.geometry_type:
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        raise ValueError("Spatial DataFrames must have the same geometry type.")
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    else:
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        raise ValueError("Merge datasets cannot merge types %s" % type(other))

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def _merge_dataframes(self, dataset_readers_list):
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    final_df = None
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    all_dfs = []
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    keys = []
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    for dataset, readers in dataset_readers_list:
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        dataset_df = readers[0]
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        for df in readers[1:]:
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            if df is None:
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                continue
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            dataset_df = dataset_df.add(df, fill_value=0.)
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        all_dfs.append(dataset_df)
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        keys.append(dataset)
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    final_df = pd.concat(all_dfs, keys=keys, names=['dataset'], sort=True)
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    return final_df.reset_index()

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def _concat(df, type):
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    if df is None:
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        df = pd.DataFrame(_object_blocks[type])
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    else:
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        _df = pd.DataFrame(_object_blocks[type])
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        df = pd.concat([df, _df], sort=True)
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    return df

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def append_data(df1, df2):
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    '''
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    Append df2 to df1
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    '''
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    df = pd.concat((df1, df2))
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    return df.groupby(df.index).first()

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def default_merger(x, y):
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	import pandas as pd
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	return pd.concat([x, y])

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def merge_lvj_dfs(df1, df2, how='outer'):
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    """
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    Merge two data frames on lvj indices.
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    By default, uses the union of the keys (an "outer" join).
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    """
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    merged = pd.merge(df1, df2, how=how, left_index=True, right_index=True)
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    merged.fillna(0, inplace=True)
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    return merged

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def merge_features(features, keep_partial_nan_rows=False):
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    """
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    Combine dataframes of features which share a datetime index.
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    Parameters
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    ----------
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    features : :any:`list` of :any:`pandas.DataFrame`
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        List of dataframes to be concatenated to share an index.
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    keep_partial_nan_rows : :any:`bool`, default False
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        If True, don't overwrite partial rows with NaN, otherwise any row with a NaN
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        value gets changed to all NaN values.
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    Returns
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    -------
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    merged_features : :any:`pandas.DataFrame`
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        A single dataframe with the index of the input data and all of the columns
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        in the input feature dataframes.
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    """
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    def _to_frame_if_needed(df_or_series):
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        if isinstance(df_or_series, pd.Series):
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            return df_or_series.to_frame()
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        return df_or_series
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    df = pd.concat([_to_frame_if_needed(feature) for feature in features], axis=1)
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    if not keep_partial_nan_rows:
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        df = overwrite_partial_rows_with_nan(df)
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    return df