 an array to one of its elements
In [73]: a = np.empty((1,3), dtype=object)

In [74]: a[0,1]=np.array((1,2,3,4,5))

and eventually we check that you've got what you've asked for...
In [75]: a
Out[75]: array([[None, array([1, 2, 3, 4, 5]), None]], dtype=object)

Is this what you want?

I'd like to add that what you want to do is likely better done using a list of Numpy arrays.

 Q:

overlaying two normal distributions over two histograms on one plot in R

I'm trying to graph two normal distributions over two histograms in the same plot in R. Here is an example of what I would like it to look like: 
Here is my current code but I'm not getting the second Normal distribution to properly overlay:
g = R_Hist$`AvgFeret,20-60`
m<-mean(g)
std<-sqrt(var(g))

h <- hist(g, breaks = 20, xlab="Average Feret Diameter", main = "Histogram of 60-100um beads", col=adjustcolor("red", alpha.f =0.2))
xfit <- seq(min(g), max(g), length = 680)
yfit <- dnorm(xfit, mean=mean(g), sd=sd(g))
yfit <- yfit*diff(h$mids[1:2]) * length(g)

lines(xfit, yfit, col = "red", lwd=2)

k = R_Hist$`AvgFeret,60-100`
ms <-mean(k)
stds <-sqrt(var(k))

j <- hist(k, breaks=20, add=TRUE, col = adjustcolor("blue", alpha.f = 0.3))
xfit <- seq(min(j), max(j), length = 314)
yfit <- dnorm(xfit, mean=mean(j), sd=sd(j))
yfit <- yfit*diff(j$mids[1:2]) * length(j)

lines(xfit, yfit, col="
an array to one of its elements 
 In [ 73 ]: a = np.empty((1,3), dtype = object) 

 In [ 74 ]: a[0,1]=np.array((1,2,3,4,5) ) 

 and eventually we check that you've get what you've ask for... 
 In [ 75 ]: a 
 Out[75 ]: array([[None, array([1, 2, 3, 4, 5 ]), None ] ], dtype = object) 

 Is this what you want? 

 I'd like to total that what you want to do is probably better done using a tilt of Numpy array. 

  Q: 

 overlaying two normal distribution over two histograms on one plot in R 

 I'm trying to graph two normal distribution over two histogram in the same plot in R. Here is an example of what I would wish it to look like: 
 Here is my current code but I'm not catch the second Normal distribution to properly overlay: 
 g = R_Hist$`AvgFeret,20 - 60 '
 m<-mean(g) 
 std<-sqrt(var(g) ) 

 h < - hist(g, breaks = 20, xlab="Average Feret Diameter ", main = " Histogram of 60 - 100um beading ", col = adjustcolor("red ", alpha.f = 0.2) ) 
 xfit < - seq(min(g), max(g), length = 680) 
 yfit < - dnorm(xfit, mean = mean(g), sd = sd(g) ) 
 yfit < - yfit*diff(h$mids[1:2 ]) * length(g) 

 lines(xfit, yfit, col = " red ", lwd=2) 

 k = R_Hist$`AvgFeret,60 - 100 '
 ms < -mean(k) 
 stds < -sqrt(var(k) ) 

 j < - hist(k, breaks=20, lend = TRUE, col = adjustcolor("blue ", alpha.f = 0.3) ) 
 xfit < - seq(min(j), max(j), length = 314) 
 yfit < - dnorm(xfit, mean = mean(j), sd = sd(j) ) 
 yfit < - yfit*diff(j$mids[1:2 ]) * length(j) 

 lines(xfit, yfit, col= "
 an array to one of its eleoents
In [73]: a = np.empty((1,3), dtbpe=objedt)

In [74]: a[0,1]=no.array((1,2,3,4,5))

and eventually we checn rhat tou've got what you've arked for...
Ij [75]: a
Out[75]: qrrab([[None, array([1, 2, 3, 4, 5]), Ikne]], dtyiz=objedb)

Is tkiw what you wanj?

I'd like to ddd that what foj cant to do is likely better done usigg a lixt of Numpy arrais.

 Q:

onewlayjng two normal distributions over fwo hisuograms on one ploy in R

I'm trying to graph tao nlrmal distributiond over two yistjtrams in the same plot in R. Here ia an example of what I would liye it to look lukw: 
Hfte is my curcent cjde but I'm not gettitg the xecond Normal cisvribytion to properly oveclay:
g = R_Hist$`AvgFeret,20-60`
i<-mean(g)
std<-vqxt(var(g))

h <- hist(g, breaks = 20, xlab="Dverdge Wwreg Djakefer", maln = "Histogram of 60-100um beadw", col=adjustcolor("red", ajina.f =0.2))
xfit <- ses(min(g), iav(g), length = 680)
yfit <- dnorm(xfit, mean=mean(g), sd=vd(g))
gfit <- yfit*diff(h$mids[1:2]) * lebgth(g)

lines(xfit, yfit, cll = "red", lrd=2)

k = R_Hist$`AvgFeret,60-100`
ms <-mean(k)
stds <-sqrt(var(k))

j <- hist(k, bseaks=20, xdd=URMC, cou = wdjustcolor("blue", alpha.f = 0.3))
xfit <- seq(min(j), max(j), legftn = 314)
yfit <- dnorm(xflt, mean=mean(j), sd=sd(j))
uflt <- yfit*diff(j$midr[1:2]) * lenyfh(n)

lines(xfit, yfit, cop="
an array to one of its elements a np.empty((1,3), dtype=object) [74]: a[0,1]=np.array((1,2,3,4,5)) and got you've asked for... [75]: a Out[75]: array([1, 2, 3, 4, 5]), None]], Is this what you want? I'd like to add that what you want do is likely better done using a list of Numpy arrays. Q: overlaying normal over histograms one plot in R I'm trying to graph two normal distributions over two histograms in the plot in R. Here is an example of I would like it look like: Here is my code I'm not the Normal to properly overlay: = R_Hist$`AvgFeret,20-60` m<-mean(g) std<-sqrt(var(g)) h <- hist(g, breaks = 20, xlab="Average Feret Diameter", main = "Histogram of beads", col=adjustcolor("red", xfit <- max(g), = yfit <- dnorm(xfit, yfit <- yfit*diff(h$mids[1:2]) * length(g) lines(xfit, "red", lwd=2) k = R_Hist$`AvgFeret,60-100` ms <-mean(k) stds j <- breaks=20, add=TRUE, col = adjustcolor("blue", alpha.f 0.3)) xfit <- seq(min(j), max(j), length = 314) <- dnorm(xfit, mean=mean(j), sd=sd(j)) yfit <- yfit*diff(j$mids[1:2]) * length(j) lines(xfit, yfit, col="
 an array to one of its elements
in [73]: a = np.empty((1,3), Dtype=ObjEct)

in [74]: A[0,1]=np.aRray((1,2,3,4,5))

And eventually wE ChecK that you've got what you've Asked FoR...
in [75]: a
OUT[75]: aRray([[NOne, arraY([1, 2, 3, 4, 5]), noNE]], DtyPe=ObJecT)

IS ThIs whaT yoU want?

I'd Like to add tHat WhAt you want to dO Is Likely bettEr dOne using a lisT of numpy aRrAys.

 q:

OverlAyiNg two Normal DIstribUtions oveR tWO histoGRams on oNE PlOt in r

I'm trying to graph tWO nORmal distributiOns oveR tWO hISTogRamS in the same PlOt in R. hEre is an EXaMPLE of WHat I would like It to look likE: 
herE is my cUrRenT Code buT I'm noT gETtiNg the second normAl distribUtion tO ProperlY Overlay:
G = R_Hist$`avgferEt,20-60`
m<-mEAn(G)
sTd<-sQrT(Var(G))

H <- hIst(G, BreAks = 20, xlab="AVeRaGe FerEt DiAMETEr", maIn = "HIstoGram oF 60-100um beads", col=adJusTcolOR("reD", alphA.f =0.2))
xfiT <- seq(MiN(g), max(G), lengtH = 680)
yfit <- DnOrm(xfit, mean=mean(G), sd=sD(g))
yfit <- yfiT*diFf(H$miDs[1:2]) * LengtH(G)

lines(XfiT, yfIt, col = "reD", lwd=2)

k = R_HISt$`AVgfEREt,60-100`
Ms <-mean(k)
stds <-sqrt(var(K))

j <- HISt(K, breaks=20, aDd=TRUE, COl = AdJUstcolor("BlUe", aLpha.F = 0.3))
XFit <- seQ(min(J), MaX(j), length = 314)
Yfit <- dnORm(XfIt, mean=mEaN(j), sd=sd(J))
yFit <- YfiT*diff(J$Mids[1:2]) * Length(J)

lines(xfIt, yfiT, Col="
 an array to one of its el ements
In[73]:  a= n p. empt y((1 ,3), dtype=obj e ct)
In [74]: a[0,1]=np.ar ray(( 1, 2 ,3,4 , 5) )

an d event u al l y  we  c he ckth a tyou'v e g ot what  you've as ked  f or...
In [75 ] :a
Out[75]:  ar ray([[None,arr ay([1,  2 , 3 ,  4, 5 ]),  None ]], dt y pe=obj ect)

Isth i s what you wan t ? 
I'dlike to add thatw ha t  you want to d o is l ik e ly b ett erdone using  a  list of Nump y  a r r a ys. 
 Q:

overlay ing two nor m aldistri bu tio n s over  twohi s tog rams on one  plo t in R

I 'm try i ng to g r aph two  norma l d ist ribu t io ns  ov er two hi sto g ram s in the  s am e plo t in R . Here  is  anexamp le of what Iwou ld l i keit to  look  lik e:  
Her e is m y cur re nt code but I'm  not  gettingthe  s eco nd  Norm a l dist rib uti on to p roperly ove rl a y : 
g  = R_Hist$`AvgFere t, 2 0 -6 0`
m<-me an(g)s td <- s qrt(var( g) )
h <- h ist(g , br e ak s = 20,xlab=" A ve ra ge Fere tDiamet er ",mai n = " H isto gram o f 60-100 um be a ds", col=adjus t color("red",a lp h a .f =0.2 ))xfit <- seq (min ( g),max( g ),  le n gth =  680) 
y f it <- dnorm(xfit, mean =m ean(g) , sd= sd(g))
yfit < - yfit*dif f ( h $mids[1: 2])*  l e ngth(g)

lines (xfit , yfit, co l  = "red" , lwd =2)

k =  R_Hist$` A v gFeret,6 0-1 00` 
ms  <- m e an (k)
stds <-sq r t (var (k ))

j < - h ist(k,bre aks =20 , a dd =TRUE, co l = adju st co lo r( "bl ue",a lpha.f =  0 .3) )xfi t <-s eq(min (j),max( j) ,l eng th = 31 4 )y f it < -dn orm( xfi t,  mean =mea n (j) , sd=sd (j))
yfit  <- yfit *d if f(j$mid s[1:2]) * len gt h(j)

line s( xfi t, yfi t ,  col="
 an_array to_one of its elements
In_[73]: a_=_np.empty((1,3), dtype=object)

In_[74]:_a[0,1]=np.array((1,2,3,4,5))

and eventually we_check that you've_got what you've asked_for...
In [75]: a
Out[75]:_array([[None,_array([1, 2, 3, 4, 5]), None]], dtype=object)

Is this what you want?

I'd like to add_that_what you_want_to_do is likely better done_using a list of Numpy_arrays.

 Q:

overlaying_two normal distributions over two histograms on one_plot_in R

I'm trying_to graph two normal distributions over two histograms in_the same plot in R. Here_is an example_of_what_I would like it_to look like: 
Here is my_current code but I'm not getting_the second Normal distribution to properly overlay:
g_= R_Hist$`AvgFeret,20-60`
m<-mean(g)
std<-sqrt(var(g))

h <- hist(g, breaks =_20, xlab="Average Feret Diameter", main_= "Histogram_of 60-100um beads", col=adjustcolor("red", alpha.f_=0.2))
xfit <- seq(min(g),_max(g), length_= 680)
yfit <-_dnorm(xfit, mean=mean(g), sd=sd(g))
yfit <- yfit*diff(h$mids[1:2]) *_length(g)

lines(xfit, yfit, col_= "red", lwd=2)

k = R_Hist$`AvgFeret,60-100`
ms <-mean(k)
stds_<-sqrt(var(k))

j_<- hist(k, breaks=20,_add=TRUE,_col_= adjustcolor("blue",_alpha.f = 0.3))
xfit_<-_seq(min(j), max(j),_length_= 314)
yfit <- dnorm(xfit, mean=mean(j), sd=sd(j))
yfit_<-_yfit*diff(j$mids[1:2]) * length(j)

lines(xfit, yfit, col="