1 Adam 213H 9. For example, for a dataframe with 80k rows, it's 30% faster 1 and for a dataframe with 800k rows, it's 60% Jul 30, 2014 · 179. Apr 15, 2021 · Selecting columns based on their name. A really simple solution here is to use filter(). Concatenate pandas objects along a particular axis. min() However, if I have more than those two columns, the other columns (e. to_list(). col_level scalar Aug 22, 2012 · By setting the index to the STK_ID column, we can use the pandas builtin slicing object . You can use the following basic syntax to split a string column in a pandas DataFrame into multiple columns: #split column A into two columns: column A and column B. DataFrame(columns=['A','B']) tmpDF[['A','B']] = df['V']. Simple but effective solution. Aug 17, 2017 · Setting it to FALSE, you preserve the structure and obtain a one-column dataframe DF[, 1, drop = FALSE] #> a #> 1 1 #> 2 2 A good explanation of this point can be found at: Advanced R by Hadley Wickham, CRC, 2015, section 3. Column (s) to explode. I would like to merge on the common column name but keep all the different columns from the second dataFrame where there's a match on the common column name. If joining columns on columns, the DataFrame indexes will be ignored. parser to do the conversion. groupby(groupby_cols, as_index=False) summed = groupby. Grouping by a DataFrame and keeping columns involves four steps: get the data, split it, apply a function, and combine the result. any() sub_df = df. Jun 19, 2017 · Previous solutions for old pandas versions: I think you can use set_index + droplevel + reset_index: a b c1 c2. g. Let’s see a few commonly used approaches to filter rows or columns of a dataframe using the indexing and selection in multiple ways. Jan 26, 2024 · The set_index() method of pandas. Keep labels from axis for which “like in label == True”. The resulting dataframe contains the ‘first Nov 8, 2016 · The idea is that instead of specifying all of the columns that you wish to delete from a DataFrame via the . However, you can use the agg() function to Jul 24, 2019 · User ID Cupcakes Biscuits Score. drop method, you specify instead the columns you wish to keep through a . Nov 6, 2023 · Pandas is a powerful tool for data analysis and manipulation. This behavior can be modified by passing in keep='last' into the method. If it's columns, then. head, n=1) This is possible because by default groupby preserves the order of rows within each group, which is stable and pandas. DataFrame allows you to set an existing column as the index (row labels). Thus, you are able to use this: Apr 3, 2017 · 1. Only consider certain columns for identifying duplicates, by default use all of the columns. csv', usecols=cols) df. Apr 12, 2024 · # Pandas: Merge only specific DataFrame columns using DataFrame. 9179 1 50. DataFrame. , coln, we have to insert all the columns that needed to be removed in a list. With the usecols parameter of the read_csv() Jul 13, 2020 · Using Pandas drop_duplicates to Keep the Last Row. *")). Returns a pandas series. Inside these brackets, you can use a single column/row label, a list of column/row labels, a slice of labels, a conditional expression or a colon. If a column of strings are compared to some other string(s) and matching rows are to be selected, even for a single comparison operation, query() performs faster than df[mask]. You can also use the DataFrame. Some code that works the same for . import pandas as pd. Parameters: levelint, str, tuple, or list, default None. tolist() Complete code: import pandas as pd. #select columns where at least one row has a value greater than 2 df. groupby("item", as_index=False)["diff"]. Examples of both methods are provided in the article. js 3 1. Jun 16, 2023 · Selecting specific columns while reading a CSV file using pandas is a common requirement in data analysis and manipulation tasks. Select specific rows and/or columns using loc when using the row and column names. I'm wondering how to apply it for paired values in multiple columns (two in this case). 1 or section 4. count()>4000. Another solution with assign: . This is normal behaviour, but it changes the data type and you have to restate what data types the columns should have. Let’s take a look at an example: Column(s) to unpivot. The join is done on columns or indexes. 423 5 58. A groupby operation involves some combination of splitting the object, applying a function, and combining the results. Drop specified labels from rows or columns. To get a filtered list of just column names df. I have a DataFrame where I would like to keep the rows when a particular variable has a NaN value and drop the non-missing values. 2266 8 46. copy() This is particularly useful if you want to conditionally drop duplicates, e. Used to determine the groups for the groupby. head(50) will do the trick. MultiIndex. Of course, you can combine them. any()] Method 2: Select Columns Where All Rows Meet Condition. Pandas provides a wide range of methods for selecting data according to the position and label of the rows and columns. Only remove the given levels from the index. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be Reset the index, or a level of it. parser. loc. If not specified, uses all columns that are not set as id_vars. name or ‘variable’. Apr 28, 2017 · data. The following tutorials explain how to perform other common operations in pandas: How to Keep Certain Columns in Pandas Oct 5, 2021 · data_unique = data3. js 2 1. So you can first manually type the columns that you want to order and to be positioned before all the other columns in a list cols_to_order. split Nov 16, 2012 · We can remove or delete a specified column or specified columns by the drop () method. 0089 4 58. The inplace argument can be used as well as for rows. # Below are quick example. reset_index() df3=df[['Misc1','Misc2']] join the two DFs. drop(columns='index') Brand Price Year Misc1 Misc2. merge(df2, 'outer') Name Addr Num Parent Parent_Addr Parent_Num. get_dummies(df) However, this makes the ID variable disappear. Considering certain columns is optional. df[['Color', 'Score']] 2. aapl GC 100 70. Jan 18, 2018 · I have a two Pandas dataframes and want to intersect (aka inner join) them on a key. Remove rows or columns by specifying label names and corresponding axis, or by directly specifying index or column names. I have a data frame df where the filtering columns are B and C (NaN represents empty cells): pandas. Another solution with select columns by ['c']: a b c1 c2. E. Example: ticker opinion x1 x2. In the columns, some columns match between the two (currency, adj date) for example. Mar 22, 2017 · 4. Note that this routine does not filter a dataframe on its contents. 4312 9 59. See examples, timings, and warnings for each approach. Then check if column contains the given sub-string or not, if yes then mark True in the boolean sequence, otherwise False. columns], names=data. One of the most basic ways in pandas to select columns from dataframe is by passing the list of columns to the dataframe object indexing operator. The only way to do this would be to include C in your groupby (the groupby function can accept a list). But if I try some data pre-processing feature of Sci-kit-learn lib, I end up losing all my headers and the frame gets converted to just a matrix of numbers. dum = pd. split(',', 1, expand=True) The following examples show how to use this syntax in practice. The default uses dateutil. 0 Matt 123H 8. The following just gives me "Unalignable boolean Jan 23, 2017 · You need assign output to new column and then remove Amount column by drop: Name Date Cumsum. To keep certain columns in a DataFrame, you can use the DataFrame. Code: df = df. abm NaN 80 90. The axis labeling information in pandas objects serves many purposes: Identifies data (i. while the return is series, not DataFrame. Then pass this Boolean sequence to loc Mar 10, 2014 · The idea is to select columns by regex. When you merge two indexed dataframes on certain values using 'outer' merge, python/pandas automatically adds Null (NaN) values to the fields it could not match on. Now that pandas' indexes support string operations, arguably the simplest and best way to select columns beginning with 'foo' is just: df. set_index('STK_ID', inplace=True) RPT_Date STK_Name sales STK_ID 0 pandas. The dataframe_name. filter(regex=("d. If the DataFrame has a MultiIndex, this method can remove one or more levels. Apr 12, 2015 · I have a pandas data frame which has some rows and columns. 0. This would save typing in cases where there are many columns, and we only want to keep a small subset of columns. keep_cols method - all other columns are deleted. rand(10, 3), columns=['alp1', 'alp2', 'bet1']) I'd like to get a dataframe containing every columns from df that have alp in their names. join(df3). groupby(['A','C'])['B']. filter = ( (df>=30) & (df<=40)). iloc[:, : 50] will work. groupby('A'). As a general note, filter is a very flexible and powerful way to select specific columns. DataFrame. loc[:, (df > 2). loc[: , filter] Index should be similar to one of the columns in this one. I'm using pandas 0. Can also replace columns= with index=. Then you construct a list for new columns by combining the rest of the columns: new_columns = cols_to_order + (frame. nlargest(n, columns, keep='first') [source] #. 2. DataFrame(df["A"]) If you only know the column names that you want to drop: new_df = df. > df ds Category X 2010-01-01 01:00:00 A 32 2010-01-01 01:00:00 B Sep 1, 2021 · df = pd. head() May 6, 2019 · I have hourly data, of variable x for 3 types, and Category column, and ds is set as index. Each column has a header. Feb 16, 2020 · A part of the answer can be found here (How to select rows from a DataFrame based on column values?), however it's only for one column. 3874 2 50. *In newer versions of pandas prefer loc or iloc to remove the ambiguity of ix as position or label: df. Update null elements with value in the same location in other. # Selecting columns by passing a list of desired columns. This is especially useful if that dataframe has unneeded columns with the same names as the columns you are removing. Something like this: df1 = df. This is more intuitive when dropping duplicates based on a subset of columns. df[['A', 'B']] = df['A']. split('/', expand=True) This will automatically create as many columns as the maximum number of fields included in any of your initial strings. Combine two DataFrame objects by filling null values in one DataFrame with non-null values from other DataFrame. total 5 -4 2 -5. For multiple columns, specify a non-empty list with each element be str or tuple, and all specified columns their list-like data on same row of the frame must have matching length. From your updated description, it looks like you're trying to drop duplicates based on two columns, which can be achieved by doing: Jun 12, 2022 · here is one way to do it. The filter is applied to the labels of the index. To specify a regular expression to match the names beginning with foo. Return a reshaped DataFrame or Series having a multi-level index with one or more new inner-most levels compared to the current DataFrame. df. Round a DataFrame to a variable number of decimal places. shape[1]) df = pd. EDIT by comment: First groupby columns Name and Date and aggregate sum, then groupby by level Name and aggregate cumsum. drop(column0, axis=1) To remove multiple columns col1, col2, . isna() & df. How to convert column values to Float; How to convert column values to Int; How to read dataframe from XML; How to save dataframe to XML file; How to join dataframes; How to replace values in dataframe; How to use multiple conditions in mask() Get current year and month; What is opposite of melt() in Pandas; How to set Jan 16, 2022 · There are multiple instances where we have to select the rows and columns from a Pandas DataFrame by multiple conditions. on str, list of str, or array-like, optional. Apr 20, 2016 · 41. Column to be removed = column0. Parameters: decimalsint, dict, Series. drop(cols_to_order). 2 ctdpf_j_cspp_instrument. Feb 12, 2023 · Select columns a containing sub-string in Pandas Dataframe. 在本文中,我们将介绍如何使用Pandas库删除DataFrame中的所有列,只保留其中特定的一些列。Pandas是一个强大的数据分析工具,可以帮助我们轻松处理数据,有助于进行数据清理和探索性分析。 阅读更多:Pandas 教程. explode. split() Specify delimiter or regular expression pattern: pat, regex. I want the dataframe to look like the following: stream_name preferred_timestamp internal_timestamp conductivity pressure salinity pandas. Suppose df is a dataframe. edit: fixed bad header usage. head: data. You can keep all the rows with an 'outer' merge. Otherwise dict and Series round to variable numbers of places. Aug 8, 2023 · When using the drop() method to delete a column, specify the column name for the first argument labels and set the axis argument to 1. Return DataFrame with duplicate rows removed. Give this a try: df. This article explains the following contents. fillna() or dropna() do not seem to preserve data types Jul 21, 2021 · by Zach Bobbitt July 21, 2021. set_index(groupby_cols) df = groupby_sum(df, ["PlatformCategory", "Platform", "ResClassName Dec 2, 2021 · Since ‘points’ and ‘assists’ both exist in the DataFrame, pandas went ahead and created a new column called ‘total’ that shows the sum of the ‘points’ and ‘assists’ columns. I have a DataFrame with an ID variable and another categorical variable. Is there a way to keep other variables. Apr 20, 2022 · That is why this tutorial explains DataFrame grouping using relatable challenges, code snippets, and solutions. 3 ctdpf_j_cspp_instrument. pandas will try to call date_parser in three different ways, advancing to the next if an exception occurs: 1) Pass one or more arrays (as defined by parse_dates) as arguments; 2) concatenate (row-wise) the string values from the columns defined by parse_dates into a single array and Apr 11, 2016 · The question is very old but wish to provide my solution to the question of "Preserving the order of columns while reading a csv file into pandas data frame": import numpy as np import pandas as pd # Get column count as a list cols = np. A named Series object is treated as a DataFrame with a single named column. Can I keep those columns using groupby, or am I Jul 12, 2017 · You could slice the ID column from df1 as a DataFrame and merge on ID: 'A': [4, 4, 1, 2, 3] }) 'B': [2, 2, 9] }) This returns a DataFrame of the form: ID B. Pandas 保留DataFrame中的特定列,删除其他所有列. Feb 22, 2013 · Using column numbers instead of names give me the same problem. And I need this ID variable later on to merge to other data sets. For example, the following code will create a new DataFrame that only keeps the "team" and "points" columns: python. I want to use it to select only the True columns to a new Dataframe. For example, suppose I have the following two dataframes, df_users and df_valid_users. 3. Indexes, including time indexes are ignored. ‘first’ : Drop duplicates except 1 ctdpf_j_cspp_instrument. DataFrame({'A':[1,2,3,4,5,6,7,8,9,10], 'B Feb 1, 2019 · The accepted answer (suggesting idxmin) cannot be used with the pipe pattern. DataFrame(df["A"], columns=df. Quick Examples of Set Order of Columns in DataFrame. drop('Amount', axis=1) Name Date Cumsum. 21. startswith('foo')] Alternatively, you can filter column (or row) labels with df. 1. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be Jan 8, 2019 · I'm using groupby on a pandas dataframe to drop all rows that don't have the minimum of a specific column. However this is not useful if you happen not to know the names of all the columns you want to drop. By default, groupby() returns a new dataframe with only the columns used to group the data and the result of the operation applied to each group. Subset the dataframe rows or columns according to the specified index labels. Oct 9, 2015 · This is because of using integer indices (ix selects those by label over -3 rather than position, and this is by design: see integer indexing in pandas "gotchas"*). In addition, Pandas also allows you to obtain a subset of data based on column types and to filter rows with boolean indexing. To select all those columns from a dataframe which contains a given sub-string, we need to apply a function on each column. Really, it's easier just to reuse the original index on subsequent groupby's, since the transform function modifies the index column even if it is kept. Determines which duplicates (if any) to keep. e. b. loc[] to set column order. random. If None it uses frame. df['hue'] Passing a list in the brackets lets you select multiple columns at the same time. The row and column indexes of the resulting DataFrame will be the union of the two. nlargest. count() if summed. Mar 26, 2015 · I want to use a boolean to select the columns with more than 4000 entries from a dataframe comb which has over 1,000 columns. sort_values('B'). round(decimals=0, *args, **kwargs) [source] #. filter(lst) and it will automatically ignore any missing columns. import numpy as np. drop_duplicates. It would be best to learn data grouping in Pandas before seeing practical examples. a. # Set the columns in a order you want in a list and assign it to DF. drop(columns='a') or df. For storing data into a new dataframe use the same approach, just with the new dataframe: tmpDF = pd. goog GC 40 60. notna() & df. isna()] This is clear and simple when you have a small number of columns that you know about ahead of time. For more, see the documentation for filter. Reset the index of the DataFrame, and use the default one instead. 2471 7 57. Each data frame is 90 columns, so I am trying to avoid writing everything out by hand. duplicated(subset=['Id'])]. If a Series is passed, its name attribute must be set, and that will be used as the column name in the resulting joined DataFrame. Feb 2, 2019 · Learn how to select or drop certain columns in a pandas DataFrame using different methods and options. I want to tidy ("melt") this data so that the dessert type are separate observations. loc indexer to select columns by label or position. str. May 18, 2018 · For example, if you have columns a, b, and c, and you want to find rows where the value in columns a is not NaN and the values in the other columns are NaN, then do the following: df[df. Sep 25, 2018 · I have a pandas dataframe which looks like the following: ID Name Value 0 Peter 21,2 1 Frank 24 2 Tom 23,21/23,60 3 Ismael 21,2/ 21,54 4 Joe 23,1 and so on What I am trying to is to split the "Value" column by the slash forward (/) but keep all the values, which do not have this kind of pattern. msft NaN 50 40. value_name scalar, default ‘value’ Name to use for the ‘value’ column, can’t be an existing column label. #only keep columns 'col1' and 'col2'. But if get it from pivot_table solution is remove [] or add parameter values='c' if missing. Like here: Feb 12, 2023 · Select dataframe columns based on multiple conditions. select_dtypes(include=np. In addition to learning how to use these techniques, you also learned about set logic by experimenting with the different ways to join your datasets. For methods to rename the index, refer to the following article. If an int is given, round each column to the same number of places. drop(labels=None, *, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise') [source] #. otherstuff in my example) get dropped. For example, one can use label based indexing with loc function. Additional Resources. 0 AA 35000 2018 Description: Car Prices 2022. Transform each element of a list-like to a row, replicating index values. This is the most basic way to select a single column from a dataframe, just put the string name of the column in brackets. You can see this stuff at Indexing and Selecting Data. Each data frame has two index levels (date, cusip). The new inner-most levels are created by pivoting the columns of the current dataframe: 1. : I wasn't sure if you meant rows or columns. Split with delimiter or regular expression pattern: str. ix[:,'c_0327':]. What is the best way to merge these by index, but to not take two copies of currency and adj date. . df1. Mar 26, 2023 · In pandas, you can split a string column into multiple columns using delimiters or regular expression patterns by the string methods str. total 5 2. Sep 15, 2020 · Filtering data from a data frame is one of the most common operations when cleaning the data. The second solution you posted worked for me. Keep labels from axis which are in items. Jan 17, 2023 · You can use the following methods to only keep certain columns in a pandas DataFrame: Method 1: Specify Columns to Keep. In the above DataFrame, I would like to drop all observations where opinion is not missing (so, I would like Mar 27, 2024 · 1. . 3874 3 55. 8227 6 55. filter(). drop() In some cases, you might need the columns for the merge operation but might want to remove the columns from the resulting DataFrame. Return the first n rows with the largest values in columns, in descending order. If you are in a hurry, below are some quick examples of how to set the order of DataFrame columns. concat(objs, *, axis=0, join='outer', ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False, sort=False, copy=None) [source] #. Number of decimal places to round each column to. Using melt() directly doesn't work: id_vars=['User ID'], value_vars=['Cupcakes', 'Biscuits'], var_name='Dessert', value_name='Enjoyment'. The pandas version used in this article is as follows. names) and then access with string instead of boolean column index values (the names=data. var_name scalar, default None. Jan 15, 2018 · 12. Indexing and selecting data. breakup your DF into two DFs, while resetting the index on first one. extract(). But I also want to keep the score for each user. Pandas also allows you to easily keep the last instance of a duplicated record. df2=df[['Brand','Price','Year']]. Enables automatic and explicit data alignment. #drop columns 'col3' and 'col4'. I can workaround the issue by dropping the dummy column after the read_csv step, but I'm trying to understand what is going wrong. If you want to split a string into more than two columns based on a delimiter you can omit the 'maximum splits' parameter. REMEMBER. This seems to be very incorrect - if you replace the column name 't' with 'td', then the regex picks up all three columns. Name to use for the ‘variable’ column. Now as long as I keep doing data manipulation operations in pandas, my variable headers are retained. index]) In this case the resulting dataframe will have the same rows of df1 but with some extras Nov 4, 2022 · You can use the following methods to select columns in a pandas DataFrame by condition: Method 1: Select Columns Where At Least One Row Meets Condition. Can also add a layer of hierarchical indexing on the concatenation axis, which may be Apr 30, 2018 · I have a dataframe (df) column of floats: 0 59. columns returns the list of all the columns in the dataframe Merge DataFrame or named Series objects with a database-style join. Using the logic explained in previous example, we can select columns from a dataframe based on multiple condition. For example, the following code drops duplicate 'a1' s from column Id (other pandas. apply(DataFrame. c. # Select columns which contains any value between 30 to 40. explode, provided all values have lists of equal size. names parameter is optional and not relevant to this example). 10. note that by default merge will join on all common column names. Column selection using column list. This can be used to group large amounts of data and compute operations on these groups. In your example, just type: df. Here's an example of the two dataFrames: 147. Stack the prescribed level (s) from columns to index. 5 in the on-line version of the book (June 2021) Jun 19, 2023 · groupby() is a powerful function in Pandas that allows you to group rows of a dataframe based on one or more columns and perform operations on each group. wmt GC 45 15. dftest = pd. merge() for combining data on common columns or indices. More of Python Pandas. #select columns where all rows have a value df = pd. sum() return (groupby. concat() for combining DataFrames across rows or columns. columns = pd. It's as if the regex doesn't start at the beginning of the column name. I want to create dummy variables out of the categorical variable with get_dummies. iloc indexer to select columns by position. iloc[-3:] see the docs. pandas. from_tuples([(str(i),str(j)) for i,j in data. df2. df[['col1', 'col2']] Method 2: Specify Columns to Drop. So basically it should look like this: df1 = df[df["total"] > 0] but it should filter on row instead of column and I can't figure it out. Sep 5, 2022 · In this tutorial we will learn how to use Python in order to slice and keep pandas columns in a DataFrame. drop() method to remove them after merging. Allows optional set logic along the other axes. Simple one-line answer to create a new dataframe with only numeric columns: df. This is only a light version of my problem, so my real dataframe will have more columns. concat([df1, df4], axis=1, join_axes=[df1. sum() whether or not the dataframe is empty: def groupby_sum(df, groupby_cols): groupby = df. A pipe-friendly alternative is to first sort values and then use groupby with DataFrame. I want to take the value and value_id columns and pivot these into new columns based off of value_id. You can use: df['column_name']. This expression gives me a Boolean (True/False) result: criteria = comb. In particular, you can use regular expressions. 0, the columns argument is also available. filter. sum() One other thing to note, if you need to work with df after the aggregation you can also use the as_index=False option to return a dataframe object. 1. Use a list to delete multiple columns at once. read_csv('train. Jan 13, 2015 · Python // Pandas - only select the rows that have certain conditions in a given column 1 Keeping only the rows that satisfies a condition with respect to an another column Aug 23, 2017 · pandas >= 1. Example: And if you want to keep some dataframe structure just completing this structure with the new columns, you can use: pd. stack. drop(columns=['a', 'b']). number) If you want the names of numeric columns: df. When selecting subsets of data, square brackets [] are used. python. df[['alcohol','hue']] Group DataFrame using a mapper or by a Series of columns. 1417 Is there a way in pandas to keep the integer portion of the number and discard the decimal, so the resulting column would look like: Apr 21, 2015 · Seems to use list vs tuple to determine if you want multiple columns (list) or referring to a multiindex (tuple). #. Jul 2, 2018 · I have two pandas dataFrames that share one common column name. DataFrame(data) display(df) I would like to specify columns that should remain in the dataframe based on a number of strings that are present in the index. If you only know the column name that you want to keep: import pandas as pd new_df = pd. sort_values(by=['Brand'] ). And I would get new dataframe df1: A C. split() and str. Feb 22, 2019 · In this case the join between the dataframes will work as a Inner join on SQL. Return the first n rows ordered by columns in descending order. 9097 10 57. Keep those columns whose index contain "Name" or "Country" should result in: data2 = {'Name': ['Jai', 'Princi', 'Gaurav', 'Anuj'], 'Country is': ['US', 'UK', 'GE', 'ET'] DataFrame. DataFrame(np. Here is where I am stuck. df = df[~df. The columns that are not specified are returned as well, but not used for ordering. It's also possible to call duplicated() to flag the duplicates and drop the negation of the flags. For example, Copy to clipboard. number). When using a multi-index, labels on different levels can be Oct 26, 2023 · 15. If that's the case, include the columns in the merge() call and use the DataFrame. drop(["B", "C"], axis=1) For your case, to keep the columns, but remove the content, one possible way is: new_df = pd. combine_first. arange(0, hmprice. The first method to keep columns in Pandas is to specify the columns that you want to keep. columns. If it's rows, then. df = df[['Courses','Fee','Discount','Duration']] # Using DataFrame. More readable: df. 0 James 543F 10. Conversely, to turn the index into a data column, use reset_index(). loc[:, df. provides metadata) using known indicators, important for analysis, visualization, and interactive console display. This example has a two-level column index, if you have more levels adjust this May 11, 2019 · I want to groupby DataFrame and get the nlargest data of column 'C'. Removes all levels by default. drop_duplicates(subset=['UserID'], keep=False) Otherwise the first duplicate occurrence will still be included in data_unique. This can be done by using the [] operator to access the columns by name. Starting from version 0. join() for combining data on a key column or an index. I understand how a Pandas dataframe merge() works, but the problem I have is that I want to keep the columns from one dataframe, not the columns from both. drop duplicates of a specific value, etc. Column or index level name(s) in the caller to join on the index in other, otherwise Logical and/or comparison operators on columns of strings. tolist()) After this, you can use the new_columns as other Merge DataFrame or named Series objects with a database-style join. In more recent versions, pandas allows you to explode multiple columns at once using DataFrame. empty else summed). Jul 23, 2016 · 9. columns) The pandas docs suggest several ways to keep an index (reset_index, as_index=False), which led me into thinking this would be the correct approach to re-using the index. xixhdvmbjmbuzgjhumat