Replace All Particular Values in a Data Frame

Replace all particular values in a data frame

Like this:

> df[df==""]<-NA
> df
     A    B
1 <NA>   12
2  xyz <NA>
3  jkl  100

Replace all specific values in data.frame with values from another data.frame sequentially R

With base R, we can use max.col to return the last column index for each row, where the 'Age' columns are not ., cbind with sequence of rows to return a row/column index, extract the elements and change the 'Age' column in 'df1', where the 'Age' is .

df1$Age <- ifelse(df1$Age == ".", df2[-1][cbind(seq_len(nrow(df2)), 
        max.col(df2[-1] != ".", "last"))], df1$Age)

df1 <- type.convert(df1, as.is = TRUE)

-output

df1
#  Sample Age
#1      1  50
#2      2  49
#3      3  30

---

or using tidyverse by reshaping into 'long' format and then do a join after sliceing the last row grouped by 'Sample'

library(dplyr)
library(tidyr)
df2 %>% 
    mutate(across(starts_with('Age'), as.integer)) %>%
    pivot_longer(cols = starts_with('Age'), values_drop_na = TRUE) %>%
    group_by(Sample) %>% 
    slice_tail(n = 1) %>% 
    ungroup %>% 
    select(-name) %>%
    right_join(df1) %>%
    transmute(Sample, Age = coalesce(as.integer(Age), value))

-output

# A tibble: 3 x 2
#  Sample   Age
#   <int> <int>
#1      1    50
#2      2    49
#3      3    30

data

df1 <- structure(list(Sample = 1:3, Age = c("50", ".", ".")), 
       class = "data.frame",
  row.names = c(NA, 
-3L))

df2 <- structure(list(Sample = 1:3, Age_1 = c(40L, 35L, 30L), Age_2 = c("42", 
"49", "."), Age_3 = c("44", ".", ".")), class = "data.frame", 
row.names = c(NA, 
-3L))

Replacing all values based on specific value in column dataframe

Based on 1) my initial idea of using multiplication instead of replace and 2) riding on @piRSquared's syntax together with 3) modification to exclude first column for operation, you can use:

df.iloc[:-1, 1:] *= df.iloc[-1, 1:]

Test run:

data = {'number': {0: '1', 1: '2', 2: '3', 3: 'result'},
 'error1': {0: 0.0, 1: 1.0, 2: 0.0, 3: 0.5},
 'error2': {0: 0.0, 1: 1.0, 2: 1.0, 3: 0.6},
 'error2040': {0: 1.0, 1: 1.0, 2: 0.0, 3: 0.001}}

df = pd.DataFrame(data)
print(df)

   number  error1  error2  error2040
0       1     0.0     0.0      1.000
1       2     1.0     1.0      1.000
2       3     0.0     1.0      0.000
3  result     0.5     0.6      0.001


df.iloc[:-1, 1:] *= df.iloc[-1, 1:]

print(df)

   number error1 error2 error2040
0       1    0.0    0.0     0.001
1       2    0.5    0.6     0.001
2       3    0.0    0.6       0.0
3  result    0.5    0.6     0.001

Replace specific values in a dataframe column using Pandas

A clean syntax for this kind of "find and replace" uses a dict, as

df.Num_of_employees = df.Num_of_employees.replace({"10-Jan": "1-10",
                                                   "Nov-50": "11-50"})

I'm trying to replace a specific value in my dataframe

You assign only the day of week column into sales, so it makes sense you get only one column. Try:

sales["day of week"]=sales["day of week"].replace(0, "Thru")

If it doesn't work (because day of week is an object type column), try:

sales["day of week"]=sales["day of week"].replace('0', "Thru")

Pandas DataFrame: replace all values in a column, based on condition

You need to select that column:

In [41]:
df.loc[df['First Season'] > 1990, 'First Season'] = 1
df

Out[41]:
                 Team  First Season  Total Games
0      Dallas Cowboys          1960          894
1       Chicago Bears          1920         1357
2   Green Bay Packers          1921         1339
3      Miami Dolphins          1966          792
4    Baltimore Ravens             1          326
5  San Franciso 49ers          1950         1003

So the syntax here is:

df.loc[<mask>(here mask is generating the labels to index) , <optional column(s)> ]

You can check the docs and also the 10 minutes to pandas which shows the semantics

EDIT

If you want to generate a boolean indicator then you can just use the boolean condition to generate a boolean Series and cast the dtype to int this will convert True and False to 1 and 0 respectively:

In [43]:
df['First Season'] = (df['First Season'] > 1990).astype(int)
df

Out[43]:
                 Team  First Season  Total Games
0      Dallas Cowboys             0          894
1       Chicago Bears             0         1357
2   Green Bay Packers             0         1339
3      Miami Dolphins             0          792
4    Baltimore Ravens             1          326
5  San Franciso 49ers             0         1003

Replace values from rows with specific values from another dataframe in R

You could use dplyr:

df %>%
  group_by(ID) %>%
  mutate(Min_Range_New = ifelse(is.na(Range), NA, min(Range, na.rm=TRUE)))

which returns

      ID Range Min_Range Min_Range_New
   <dbl> <dbl>     <dbl>         <dbl>
 1     1    10        10            10
 2     1    15        10            10
 3     1    20        10            10
 4     2    30        30            30
 5     2    35        30            30
 6     3    40        40            40
 7     3    45        40            40
 8     3    50        40            40
 9     3    NA        NA            NA
10     4    NA        NA            NA
11     4    NA        NA            NA

---

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