Information column is Categorical-type and takes on a value of “left_only” for observations whose merge key only appears in ‘left’ DataFrame, “right_only” for observations whose merge key only appears in ‘right’ DataFrame, and “both” if the observation’s merge key is found in both. If string, column with information on source of each row will be added to output DataFrame, and column will be named value of string.
If True, adds a column to output DataFrame called “_merge” with information on the source of each row. You can change the “indicator=True” clause to another string, such as indicator=’Check’ĭefinition of the indicator variable in the document: It’s because “_merge” already exists in the dataframe. ValueError: Cannot use name of an existing column for indicator column The key variable could be string in one dataframe, and int64 in another one.ĭf_pop = pd.DataFrame()ĭf_pop=df_pop.astype(int)Ģ. The problem is caused by different data types. If you wish to proceed you should use pd.concat ValueError: You are trying to merge on int64 and object columns. If you wish to proceed you should use pd.concatĭf_import_month_DESC_pop = df_import_month_rge(df_pop, left_on='stat_year', right_on='Year', how='left', indicator=True) Here are some problems I had before when using the merge functions:ġ.
COMBINE TWO DATAFRAMES PANDAS CODE
I used the following code to remove extra spaces, then merged them again.ĭf = df.str.replace(' ', '') I found that my State column in the second dataframe has extra spaces, which caused the failure. pandas provides various facilities for easily combining together Series or DataFrame with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations. In the beginning, the merge function failed and returned an empty dataframe. I would like to merge them based on county and state.ĭf = df.merge(temp_fips, left_on=, right_on=, how='left' ) Second dataframe temp_fips has 5 colums, including county and state. First dataframe df has 7 columns, including county and state.
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