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I have two dataframes A and B.
Dataframe A has 4 columns with 2 sets of maximum and minimums that I want to use as upper and lower bounds for 2 columns in dataframe B.
latitude = data['y']
longitude = data['x']
upper_lat = coords['lat_max']
lower_lat = coords['lat_min']
upper_lon = coords['long_max']
lower_lon = coords['long_min']
def filter_data_2(filter, upper_lat, lower_lat, upper_lon, lower_lon, lat, lon):
v = filter[(lower_lat <= lat <= upper_lat ) & (lower_lon <= lon <= upper_lon)]
return v
newdata = filter_data_2(data, upper_lat, lower_lat, upper_lon, lower_lon, latitude, longitude)
ValueError: Can only compare identically-labeled Series objects
MWE:
import pandas as pd
a = {'lower_lon': [2,4,6], 'upper_lon': [4,6,10], 'lower_lat': [1,3,5], 'upper_lat': [3,5,7]}
constraints = pd.DataFrame(data=a)
constraints
lower_lon upper_lon lower_lat upper_lat
0 2 4 1 3
1 4 6 3 5
2 6 10 5 7
b = {'lon' : [3, 5, 7, 9, 11, 13, 15], 'lat': [2, 4, 6, 8, 10, 12, 14]}
to_filter = pd.DataFrame(data=b)
to_filter
lon lat
0 3 2
1 5 4
2 7 6
3 9 8
4 11 10
5 13 12
6 15 14
lat = to_filter['lat']
lon = to_filter['lon']
lower_lon = constraints['lower_lon']
upper_lon = constraints['upper_lon']
lower_lat = constraints['lower_lat']
upper_lat = constraints['upper_lat']
v = to_filter[(lower_lat <= lat) & (lat <= upper_lat) & (lower_lon <= lon) & (lon <= upper_lon)]
Expected Results
v
lon lat
0 3 2
1 5 4
2 7 6
The global filter will be the union of the sets of all the contraints, in pandas you could:
v = pd.DataFrame()
for i in constraints.index:
# Current constraints
min_lon, max_lon, min_lat, max_lat = constraints.loc[i, :]
# Apply filter
df = to_filter[ (to_filter.lon>= min_lon & to_filter.lon<= max_lon) & (to_filter.lat>= min_lat & to_filter.lat<= max_lat) ]
# Join in a single df previous and current filter outcome
v= pd.concat( [v, df] )
# Remove duplicates, if any
v = v.drop_duplicates()
library(tidyverse)
set.seed(12345)
dat <- data.frame(year = c(rep(1990, 100), rep(1991, 100), rep(1992, 100)),
fish_length = sample(x = seq(from = 10, 131, by = 0.1), 300, replace = F),
nb_caught = sample(x = seq(from = 1, 200, by = 0.1), 300, replace = T),
stringsAsFactors = F) %>%
mutate(age = ifelse(fish_length < 20, 1,
ifelse(fish_length >= 20 & fish_length < 100, 2,
ifelse(fish_length >= 100 & fish_length < 130, 3, 4)))) %>%
arrange(year, fish_length)
head(dat)
year fish_length nb_caught age
1 1990 10.1 45.2 1
2 1990 10.7 170.0 1
3 1990 10.9 62.0 1
4 1990 12.1 136.0 1
5 1990 14.1 80.8 1
6 1990 15.0 188.9 1
dat %>% group_by(year) %>% summarise(ages = n_distinct(age)) # Only 1992 has age 4 fish
# A tibble: 3 x 2
year ages
<dbl> <int>
1 1990 3
2 1991 3
3 1992 4
dat %>% filter(age == 4) # only 1 row for age 4
year fish_length nb_caught age
1 1992 130.8 89.2 4
Here:
year = year of sampling
fish_length = length of the fish in cm
nb_caught = number of fish caught following the use of an age-length key, hence explaining the presence of decimals
age = age of the fish
graph1: geom_violin not using the weight aesthetic.
Here, I got to copy each line of dat according to the value found in nb_caught.
dim(dat) # 300 rows
dat_graph1 <- dat[rep(1:nrow(dat), floor(dat$nb_caught)), ]
dim(dat_graph1) # 30932 rows
dat_graph1$nb_caught <- NULL # useless now
sum(dat$nb_caught) - nrow(dat_graph1) # 128.2 rows lost here
Since I have decimal values of nb_caught, I took the integer value to create dat_graph1. I lost 128.2 "rows" in the process.
Now for the graph:
dat_tile <- data.frame(year = sort(unique(dat$year))[sort(unique(dat$year)) %% 2 == 0])
# for the figure's background
graph1 <- ggplot(data = dat_graph1,
aes(x = as.factor(year), y = fish_length, fill = as.factor(age),
color = as.factor(age), .drop = F)) +
geom_tile(data = dat_tile, aes(x = factor(year), y = 1, height = Inf, width = 1),
fill = "grey80", inherit.aes = F) +
geom_violin(draw_quantiles = c(0.05, 0.5, 0.95), color = "black",
scale = "width", position = "dodge") +
scale_x_discrete(expand = c(0,0)) +
labs(x = "Year", y = "Fish length", fill = "Age", color = "Age", title = "graph1") +
scale_fill_brewer(palette = "Paired", drop = F) + # drop = F for not losing levels
scale_color_brewer(palette = "Paired", drop = F) + # drop = F for not losing levels
scale_y_continuous(expand = expand_scale(mult = 0.01)) +
theme_bw()
graph1
graph1
Note here that I have a flat bar for age 4 in year 1992.
dat_graph1 %>% filter(year == 1992, age == 4) %>% pull(fish_length) %>% unique
[1] 130.8
That is because I only have one length for that particular year-age combination.
graph2: geom_violin using the weight aesthetic.
Now, instead of copying each row of dat by the value of number_caught, let's use the weight aesthetic.
Let's calculate the weight wt that each line of dat will have in the calculation of the density curve of each year-age combinations.
dat_graph2 <- dat %>%
group_by(year, age) %>%
mutate(wt = nb_caught / sum(nb_caught)) %>%
as.data.frame()
head(dat_graph2)
year fish_length nb_caught age wt
1 1990 10.1 45.2 1 0.03573123
2 1990 10.7 170.0 1 0.13438735
3 1990 10.9 62.0 1 0.04901186
4 1990 12.1 136.0 1 0.10750988
5 1990 14.1 80.8 1 0.06387352
6 1990 15.0 188.9 1 0.14932806
graph2 <- ggplot(data = dat_graph2,
aes(x = as.factor(year), y = fish_length, fill = as.factor(age),
color = as.factor(age), .drop = F)) +
geom_tile(data = dat_tile, aes(x = factor(year), y = 1, height = Inf, width = 1),
fill = "grey80", inherit.aes = F) +
geom_violin(aes(weight = wt), draw_quantiles = c(0.05, 0.5, 0.95), color = "black",
scale = "width", position = "dodge") +
scale_x_discrete(expand = c(0,0)) +
labs(x = "Year", y = "Fish length", fill = "Age", color = "Age", title = "graph2") +
scale_fill_brewer(palette = "Paired", drop = F) + # drop = F for not losing levels
scale_color_brewer(palette = "Paired", drop = F) + # drop = F for not losing levels
scale_y_continuous(expand = expand_scale(mult = 0.01)) +
theme_bw()
graph2
dat_graph2 %>% filter(year == 1992, age == 4)
year fish_length nb_caught age wt
1 1992 130.8 89.2 4 1
graph2
Note here that the flat bar for age 4 in year 1992 seen on graph1 has been dropped here even though the line exists in dat_graph2.
My questions
Why is the age 4 in 1992 level dropped when using the weight aesthetic? How can I overcome this?
Why are the two graphs not visually alike even though they used the same data?
Thanks in advance for your help!
1.
Problem 1 is not related to using the weight aesthetic. You can check this by dropping the weight aesthetic in the code for your second graph. The problem is, that the algorithm for computing the density fails, when there are too less observations.
That is the reason, why group 4 shows up in graph 1 with the expanded dataset (grpah 1). Here you increase the number of observations by replicating the number of obs.
Unfortunately, geom_violin gives no warning in your specific case. However, if you filter dat_graph2 for age == 4 geom_violin gives you the warning
Warning message:
Computation failed in `stat_ydensity()`:
replacement has 1 row, data has 0
geom_density is much clearer on this issue, giving a warning, that groups with less than two obs have been dropped.
Unfortunately, I have no solution to overcome this, besides working with the expanded dataset.
2.
Concerning problem 2 I have no convincing answer except that I guess that this is related to the details of the kernel density estimator used by geom_violin, geom_density, ... and perhaps also somehow related to the number of data points.
Consider there are two columns A and B in a dataframe. How can I decile column A and use those breakpoints of column A deciles to calculate the count of rows in column B??
import pandas as pd
import numpy as np
df=pd.read_excel("E:\Sai\Development\UCG\qcut.xlsx")
df['Range']=pd.qcut(df['a'],10)
df_gb=df.groupby('Range',as_index=False).agg({'a':[min,max,np.size]})
df_gb.columns = df_gb.columns.droplevel()
df_gb=df_gb.rename(columns={'':'Range','size':'count_A'})
df['Range_B']=0
df['Range_B'].loc[df['b']<=df_gb['max'][0]]=1
df['Range_B'].loc[(df['b']>df_gb['max'][0]) & (df['b']<=df_gb['max'][1])]=2
df['Range_B'].loc[(df['b']>df_gb['max'][1]) & (df['b']<=df_gb['max'][2])]=3
df['Range_B'].loc[(df['b']>df_gb['max'][2]) & (df['b']<=df_gb['max'][3])]=4
df['Range_B'].loc[(df['b']>df_gb['max'][3]) & (df['b']<=df_gb['max'][4])]=5
df['Range_B'].loc[(df['b']>df_gb['max'][4]) & (df['b']<=df_gb['max'][5])]=6
df['Range_B'].loc[(df['b']>df_gb['max'][5]) & (df['b']<=df_gb['max'][6])]=7
df['Range_B'].loc[(df['b']>df_gb['max'][6]) & (df['b']<=df_gb['max'][7])]=8
df['Range_B'].loc[(df['b']>df_gb['max'][7]) & (df['b']<=df_gb['max'][8])]=9
df['Range_B'].loc[df['b']>df_gb['max'][8]]=10
df_gb_b=df.groupby('Range_B',as_index=False).agg({'b':np.size})
df_gb_b=df_gb_b.rename(columns={'b':'count_B'})
df_final = pd.concat([df_gb, df_gb_b], axis=1)
df_final=df_final[['Range','count_A','count_B']]
Is there any simple solution, as I intend to do for so many columns
I hope this would help:
df['Range'] = pd.qcut(df['a'], 10)
df2 = df.groupby(['Range'])['a'].count().reset_index().rename(columns = {'a':'count_A'})
for item in df2['Range'].values:
df2.loc[df2['Range'] == item, 'count_B'] = df['b'].apply(lambda x: x in item).sum()
df2 = df2.sort_values('Range', ascending = True)
if you want to additionally count values b that are out of range a:
min_border = df2['Range'].values[0].left
max_border = df2['Range'].values[-1].right
df2.loc[0, 'count_B'] += df.loc[df['b'] <= min_border, 'b'].count()
df2.iloc[-1, 2] += df.loc[df['b'] > max_border, 'b'].count()
One way -
df = pd.DataFrame({'A': np.random.randint(0, 100, 20), 'B': np.random.randint(0, 10, 20)})
bins = [0, 1, 4, 8, 16, 32, 60, 100, 200, 500, 5999]
labels = ["{0} - {1}".format(i, j) for i, j in zip(bins, bins[1:])]
df['group_A'] = pd.cut(df['A'], bins, right=False, labels=labels)
df['group_B'] = pd.cut(df.B, bins, right=False, labels=labels)
df1 = df.groupby(['group_A'])['A'].count().reset_index()
df2 = df.groupby(['group_B'])['B'].count().reset_index()
df_final = pd.merge(df1, df2, left_on =['group_A'], right_on =['group_B']).drop(['group_B'], axis=1).rename(columns={'group_A': 'group'})
print(df_final)
Output
group A B
0 0 - 1 0 1
1 1 - 4 1 3
2 4 - 8 1 9
3 8 - 16 2 7
4 16 - 32 3 0
5 32 - 60 7 0
6 60 - 100 6 0
7 100 - 200 0 0
8 200 - 500 0 0
9 500 - 5999 0 0
Suppose I have two feature maps F1 and F2 output by a network. I want to compute convolution of F1 and F2. Assume that F1 has shape (1, C, 10, 10) and F2 has shape (1, C, 3, 3) and the wanted result should have shape (1, 1, 8, 8) if pad = 0, stride = 1 and dilate = 1. But, in this way I can only set batchsize to 1 because the kernel of Convolution layer is irrelevant of batchsize, so I cannot set the weights with a batch of output data.
How to implement this using MXNet?
I have come up with one possible way that uses mx.sym.Correlation, but I cannot get the idea how correlation operator computes by reading the doc.
Or, can I set the weight of a mx.sym.Convolution layer to F2, and data to F1? Would this interfere the propagation of grads when training?
[Update]
What I want to do is like the following example:
By correlation, I mean F2 acts like a correlation kernel (or convolution kernel) that slides on F1. For example,
1 1 1 2 2
F1 = 2 3 4 1 1
0 0 0 2 3
0 1 0
F2 = 1 0 1
0 1 0
Then, the correlation result should be
R = F1 * F2 = 7 5 9
where
1 1 1 0 1 0
7 = 2 3 4 x 1 0 1 = 1 + 2 + 4 + 0
0 0 0 0 1 0
1 1 2 0 1 0
5 = 3 4 1 x 1 0 1 = 1 + 3 + 1 + 0
0 0 2 0 1 0
1 2 2 0 1 0
9 = 4 1 1 x 1 0 1 = 2 + 4 + 1 + 2
0 2 3 0 1 0
In the above example, stride = 1, pad = 0, dilate = 0
You should be able to use mx.sym.Convolution directly for this (with batch_size >= 1).
Working with mx.nd.NDArray so we can more easily inspect the arrays, the final output shape is (batch_size, num_filters, 8, 8), i.e. (1, 1, 8, 8).
You'll be able to replace mx.nd with mx.sym to use mx.sym.Symbol. And the kernel weights will trainable.
import mxnet as mx
import numpy as np
num_batches = 1
num_channels = 3 # called C in question
num_filters = 1
kernel_shape = (3, 3)
data_shape = (10, 10)
data = mx.nd.random_uniform(shape=(num_batches, num_channels) + data_shape)
# called f1 in question
print("data (f1) shape: " + str(data.shape))
# >>>> data (f1) shape: (1, 3, 10, 10)
weights = mx.nd.random_uniform(shape=(num_filters, num_channels) + kernel_shape)
# called f2 in question
print("weights (f2) shape: " + str(weights.shape))
# >>>> weights (f2) shape: (1, 3, 3, 3)
conv = mx.nd.Convolution(data=data, weight=weights, num_filter=num_filters, kernel=kernel_shape, no_bias=True)
print("convolution output shape: " + str(conv.shape))
# >>>> convolution output shape: (1, 1, 8, 8)
I'm new to Excel VBA and i'm trying to make a loop that sums X = X + 1 but when the loop ends it continues with the last X and doesn't starts again.
This is what I have:
For I = 1 To 3
J = 2
For K = 1 To J * 2 Step 1
Debug.Print K
Next K
Next I
This is what i get: 1 2 3 4 1 2 3 4 1 2 3 4 .
What i would like to get is: 1 2 3 4 5 6 7 8 9 10 11 12 .
Thanks for the help provided. I thought this would solve my problem but it's a bit more complicated. I need this because i'm adding coordinates in X, Y, Z format with this code:
For I = 1 To 6
X = 0
J = 10
RobApp.Project.Structure.Nodes.Create X = X + 1, 0, 0, J * (I - 1)
RobApp.Project.Structure.Nodes.Create X = X + 1, Range("N34") * 0.15, 0, J *
(I - 1)
Next I
"X = X+1" is the node number. I want it to be sequencial, 1,2,3,4 and so on while J is increasing in the Z coordinate. For example for the first line of code:
Node 1 = 0,0,0
Node 2 = 0,0,10
Node 3 = 0,0,20
and so on!
Or rather, use the extra variable X as you originally planned:
X = 0
For I = 1 To 3
J = 2
For K = 1 To J * 2 Step 1
X = X + 1
Debug.Print X
Next K
Next I