Plot Timeseries Data where the hourly variations are values in different columns

If need bars with separate plots for Items use:

df1 = df.melt(['Date','Item'])
g = sns.FacetGrid(df1, row='Item', col="Date")
g.map_dataframe(sns.barplot, x="variable", y="value")

Or if need both Items in one graph use:

df1 = df.melt(['Date','Item'])
g = sns.FacetGrid(df1, col="Date")
g.map_dataframe(sns.barplot, x="variable", y="value", hue="Item")

With R and ggplot2, you have to pivot to long format for plotting.

library(tidyverse)

df %>%
  pivot_longer(-c(Date, Item), names_to = "time", values_to = "value") %>%
  mutate(time = ymd_hm(paste("0000-01-01",time, sep = " "))) %>%
  ggplot(aes(x = time, y = value)) +
  geom_line(aes(colour = Date)) +
  scale_x_datetime( breaks=date_breaks("60 min"), labels = date_format("%H:%M")) +
  theme(axis.text.x=element_text(size=8, angle = 90),
        axis.text.y=element_text(size=8),
        axis.title=element_text(size=10)) +
  facet_grid(~Item, scale='free_x')

Another option if you want both series on the same plot.

library(tidyverse) 

df %>%
  pivot_longer(-c(Date, Item), names_to = "time", values_to = "value") %>%
  mutate(Date = as.Date(Date, "%m/%d/%Y")) %>%
  mutate(time = as.POSIXct(paste(ok$Date, ok$time), format="%Y-%m-%d %H:%M")) %>%
  ggplot(aes(x = time, y = value, colour = Item, group = Item)) +
  geom_line()

*Note: Since it is just a sample of the data, there are time gaps, hence the sharp angles.

Or if you want them separate, then you can facet the plot (also using bar for another alternative).

df %>%
  pivot_longer(-c(Date, Item), names_to = "time", values_to = "value") %>%
  mutate(Date = as.Date(Date, "%m/%d/%Y")) %>%
  mutate(time = as.POSIXct(paste(ok$Date, ok$time), format="%Y-%m-%d %H:%M")) %>%
  ggplot(aes(x = time, y = value)) +
  geom_bar(stat = "identity", aes(colour = Item, fill = Item)) +
  facet_grid(~Item, scale='free_x')

Data

df <- structure(list(Date = c("1/1/2022", "1/2/2022", "1/3/2022", "1/4/2022", 
"1/1/2022", "1/2/2022", "1/3/2022", "1/4/2022"), Item = c("foo", 
"foo", "foo", "foo", "bar", "bar", "bar", "bar"), `0:00` = c(2, 
1.5, 1, 2, 3, 2.5, 1, 2), `1:00` = c(3L, 1L, 2L, 1L, 1L, 4L, 
2L, 1L), `2:00` = c(4L, 3L, 3L, 3L, 0L, 3L, 1L, 3L), `3:00` = c(1, 
2, 1, 4, 1.5, 1, 1, 2), `4:00` = c(5, 2.5, 2, 1, 5, 2.5, 2, 1
), `5:00` = c(1.5, 6, 5, 2.4, 1.5, 0, 1.5, 2.5), `6:00` = c(2.5, 
4, 4, 3, 2.5, 1, 4, 3)), class = "data.frame", row.names = c(NA, 
-8L))

In R just use matplot, no need for data wrangling beforehand.

par(mfrow=c(1, 2)); by(df[3:9], df$Item, \(x) matplot(t(x), type='l'))

Or, more publishable:

par(mfrow=c(1, 2))
by(df, df$Item, \(x) {
  matplot(t(x[1:4, 3:9]), type='l', main=el(x$Item), xaxt='n', xlab='t', ylab='y')
  axis(1, axTicks(1), colnames(df)[3:9])
  legend('topleft', leg=x$Date, col=seq_len(nrow(x)), lty=seq_len(nrow(x)), 
         cex=.8, ncol=2)
  })

Data:

df <- read.table(text="
                 Date Item 0:00 1:00 2:00 3:00 4:00 5:00 6:00
1 1/1/2022  foo  2.0    3    4  1.0  5.0  1.5  2.5
2 1/2/2022  foo  1.5    1    3  2.0  2.5  6.0  4.0
3 1/3/2022  foo  1.0    2    3  1.0  2.0  5.0  4.0
4 1/4/2022  foo  2.0    1    3  4.0  1.0  2.4  3.0
5 1/1/2022  bar  3.0    1    0  1.5  5.0  1.5  2.5
6 1/2/2022  bar  2.5    4    3  1.0  2.5  0.0  1.0
7 1/3/2022  bar  1.0    2    1  1.0  2.0  1.5  4.0
8 1/4/2022  bar  2.0    1    3  2.0  1.0  2.5  3.0
           ", check.names=F)