Write a Data Frame to CSV File Without Column Header in R

Write a data frame to csv file without column header in R

Dont use write.csv, use write.table

 write.table( <yourdf>, sep=",",  col.names=FALSE)

You cannot change many settings in write.csv -- the arguments are just there as a reminder to the user. From the documentation:

These wrappers are deliberately inflexible: they are designed to ensure that the correct conventions are used to write a valid file. Attempts to change append, col.names, sep, dec or qmethod are ignored

Export CSV without col.names

If you can't beat 'em, join 'em.

If you switch to write.table() (which write.csv() calls anyway) you're golden:

R> write.table(trees, file="/tmp/trees.csv", 
+              row.names=FALSE, col.names=FALSE, sep=",")
R> system("head /tmp/trees.csv")
8.3,70,10.3
8.6,65,10.3
8.8,63,10.2
10.5,72,16.4
10.7,81,18.8
10.8,83,19.7
11,66,15.6
11,75,18.2
11.1,80,22.6
11.2,75,19.9
R>

write.csv is messing header names when writing data frame to disk

We need to extract the list elements with [[ as [ is still a list with a single data.frame

for (i in seq_along(data_frames)) {
  file_name<- paste0("data/to_save/",names(data_frames[i]),".csv")
   write.csv(data_frames[[i]], file = file_name, row.names = FALSE, col.names = TRUE)
} 

How to Import Dataset without header and names separate files in R and attach the names to dataset?

The .data file is merely a CSV without columns headers. The .names file is not in a standard format (that I recognize), so I read the file and assigned names manually.

dat <- read.csv("~/Downloads/breast-cancer.data", header=FALSE)
names(dat) <- c("class", "age", "menopause", "tumor_size", "inv_nodes", "node_caps", "deg_malig", "breast", "breast_quad", "irradiat")
head(dat)
#                  class   age menopause tumor_size inv_nodes node_caps deg_malig breast breast_quad irradiat
# 1 no-recurrence-events 30-39   premeno      30-34       0-2        no         3   left    left_low       no
# 2 no-recurrence-events 40-49   premeno      20-24       0-2        no         2  right    right_up       no
# 3 no-recurrence-events 40-49   premeno      20-24       0-2        no         2   left    left_low       no
# 4 no-recurrence-events 60-69      ge40      15-19       0-2        no         2  right     left_up       no
# 5 no-recurrence-events 40-49   premeno        0-4       0-2        no         2  right   right_low       no
# 6 no-recurrence-events 60-69      ge40      15-19       0-2        no         2   left    left_low       no

How do you remove the column name row when exporting a pandas DataFrame?

You can write to csv without the header using header=False and without the index using index=False. If desired, you also can modify the separator using sep.

CSV example with no header row, omitting the header row:

df.to_csv('filename.csv', header=False)

TSV (tab-separated) example, omitting the index column:

df.to_csv('filename.tsv', sep='\t', index=False)

Import multiple CSVs without headers into single R data frame

If at the moment your files have different headers, then I think we can use

do.call(rbind, lapply(file_names, read.csv, skip = 1, header = FALSE))

providing that all your files have equal number of columns and same data class. The skip = 1L ignores existing header in each file, while header = FALSE will automatically generates V1, V2, ..., as column names, consistent for all data frames.

But if your files have no headers, you just need to set

do.call(rbind, lapply(file_names, read.csv, header = FALSE))

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Oh, as user20650 kindly reminded, you need the second option.

How to read csv without header and name them with names while reading in pyspark?

You can import the csv file into a dataframe with a predefined schema. The way you define a schema is by using the StructType and StructField objects. Assuming your data is all IntegerType data:

from pyspark.sql.types import StructType, StructField, IntegerType

schema = StructType([
    StructField("member_srl", IntegerType(), True),
    StructField("click_day", IntegerType(), True),
    StructField("productid", IntegerType(), True)])

df = spark.read.csv("user_click_seq.csv",header=False,schema=schema)

should work.

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