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I have around 100 dataframes with same structure, like d1, d2 ,d3, ..., d10, d11, ..., d100. I have to rbind them together, like rbind(d1,d2,.....dxx).

I don't want to manually write all dataframes names because in that case I have manually write more than 100 dataframe names and that number could increase in future. can you please help write an automatic way to rbind(d1,d2,d3,...,d10, d11,.....,d100,....)?

First create a character vector of all objects that you want to bind, like:

NameDf <- paste("d", 1:100, sep = "") 

Now, first call each object using get function and bind them together using do.call

NewDf <- do.call(cbind, lapply(NamesDf, FUN = function(x) get(x)))

Rename Multiple pandas Dataframe Column Names, Import modules import pandas as pd # Set ipython's max row display pd.​set_option('display.max_row', 1000) # Set iPython's max column width  While analyzing the real datasets which are often very huge in size, we might need to get the column names in order to perform some certain operations. Let’s discuss how to get column names in Pandas dataframe. First, let’s create a simple dataframe with nba.csv file.

We can use mget to return a list of values

out <- do.call(rbind, mget(paste0("d", 1:100)))

Python, Method #1: Changing the column name and row index using df.columns and df.​index attribute. In order to change the column names, we provide a Python list  By Andrie de Vries, Joris Meys . Variables in a data frame in R always need to have a name. To access the variable names, you can again treat a data frame like a matrix and use the function colnames() like this:

Building on akrun's elegant answer (using mget()), but using dplyr's efficient bind implementation to avoid the do.call():

library(dplyr)
mget(paste0("d",1:100)) %>% bind_rows()

How to rename columns in Pandas DataFrame, Unlike two dimensional array, pandas dataframe axes are labeled. Method #1: Using rename() function. One way of renaming the columns in a Pandas dataframe  T. Transpose index and columns. at. Access a single value for a row/column label pair. attrs. Dictionary of global attributes on this object. axes. Return a list representing the axes of the DataFrame.

Example - from my work:

filename_vector <- paste0(i, sep="_", df$unique.label.within.df, 
                          sep="", "intended.filename.csv")

pandas.DataFrame.add_prefix, DataFrame.rename · pandas.DataFrame.rename_axis · pandas.DataFrame.​reset_index · pandas.DataFrame.sample · pandas.DataFrame.set_axis · pandas. d = {} for name in companies: d[name] = pd.DataFrame() Nowadays you can write a single dict comprehension expression to do the same thing, but some people find it less readable: d = {name: pd.DataFrame() for name in companies} Once d is created the DataFrame for company x can be retrieved as d[x], so you can look up a specific company quite easily. To operate on all companies you would typically use a loop like:

Intro to data structures, The Series name will be assigned automatically in many cases, in particular when taking 1D slices of DataFrame as you will see below. You can rename a  The columns can also be renamed by directly assigning a list containing the new names to the columns attribute of the dataframe object for which we want to rename the columns. The disadvantage with this method is that we need to provide new names for all the columns even if want to rename only some of the columns.

Indexing and selecting data, You can pass a list of columns to [] to select columns in that order: If a column is not directly into Series or DataFrame to construct a MultiIndex automatically:. A data frame has (by definition) a vector of row names which has length the number of rows in the data frame, and contains neither missing nor duplicated values. Where a row names sequence has been added by the software to meet this requirement, they are regarded as ‘automatic’.

Essential basic functionality, Passing a dictionary of column names to a scalar or a list of scalars, Perhaps most importantly, these methods exclude missing/NA values automatically. Duplicate column names are allowed, but you need to use check.names = FALSE for data.frame to generate such a data frame. However, not all operations on data frames will preserve duplicated column names: for example matrix-like subsetting will force column names in the result to be unique.